CSIS Discussion Paper No. 47
An Approach to Extracting Unexpected Patterns from Massive Attributes:
Understanding of Condominium Purchasers ' Behavior through Data Mining
May, 2002
Jungmin Choi*, Asami Yasushi*** Department of Urban Engineering, University of Tokyo** Center for Spatial Information Science, University of Tokyo

Center for Spatial Information Science
University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan1

An Approach to Extracting Unexpected Patterns from Massive Attributes:
Understanding of Condominium Purchasers ' Behavior through Data Mining1
Jungmin Choi2, Asami Yasushi3
ABSTRACT
Marketing analysis is an indispensable step to establish strategies for the condominium
supply for developers. In this context, it is crucial to grasp the potential buyers’ behavioral
characteristics as “rule” or “pattern” extracted from historical transaction data. The questionthat is often asked by developers is what kind of condominium customers are pursuing andwho they are. So far, strategies in housing supply side rely mainly on the experience or
intuition of the marketers/developers, and not from the comprehensive data analysis. In thispaper, a new approach is demonstrated to extract informative and unexpected rules/patterns
from a database with relatively small contract records but massive attributes. This approach
can summarize information targeted to unexpected patterns in data structure using data
mining methodology. For the validity of the proposed method, real data of about 800
condominium purchasers in Tokyo metropolitan area is analyzed. Our approach may
contribute to develop the decision support systems that can provide managers with moreinnovative ideas from collected data in generating marketing strategies.
Keywords: Behavioral Characteristics of Purchasers, Data mining, Unexpected Patterns,
Condominium, Marketing

The authors would like to thank Recruit Co. Ltd., for kindly providing their invaluable data.
Graduate Student M.Eng., Department of Urban Engineering, University of Tokyo, 7-3-1, Hongo,
Bunkyo-ku, Tokyo, 113-8656 (haesong@ua.t.u-tokyo.ac.jp).
3 Professor Ph.D., Center for Spatial Information Science, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku,
Tokyo 113-8656, Japan (asami@csis.u-tokyo.ac.jp).
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1. INTRODUCTION
Recently there has been an apparent move of many industries towards customer oriented
approach. In the midst of increasing competition, there is indeed a need to better understandof customers, and to quickly respond to their individual needs and wants. In the housing
industry, especially the case of condominium market, this approach is still at its infancy stage.
Nevertheless, due to many survey activities conducted for newly built condominium
purchasers at contract stage, there is a wealth of data available to gain a better understandingof customer types and customer behavior. Most of major real estate companies in Japan havecollected this sort of customer database in order to reflect customers’ needs and tastes indesigning of unit plan and ultimately to improve sales of their newly built condominiums.
Recently there is new trend of supply like super high-rise condominiums located in city
centers, large scaled buildings, open plan, etc in Japan. These may be the typical illustrationsof what are consumers’ new needs and requires which should be reflected in the newmarketing strategies.
In general a detached house is a unique product which cannot be substituted, whereas in
the case of condominium it has the characteristic of general goods because the process fromdesigning to construction is typical and similar to the purchase of general goods. This implies
that condominium business has similar aspects of selling branded goods and as such, market
analysis is indispensable element in planning business strategies. This kind of recognition
encourages real estate companies to start gathering significant number of large andheterogeneous databases related to their properties. On occasion, the collected databases need
to be analyzed and applied to develop new business strategies and to identify opportunities,
but the problem that most of them faced is that “We are rich in data and poor in information”(Bounsaythip and Rinta-Runsala, 2001).
Data mining has attracted marketers as a solution to this problem. In business, thisemerging technology has been focused in market segmentation, customer profiling, risk
analysis, and many other applications. It seems that the importance and the potential
capacities of data mining have been well recognized to decision makers of housing market
particularly in private sector, but in reality the collected data are not fully employed for thispurpose. One of the reasons may be attributed to the lack of knowledge about data mining of
the marketers and of proper techniques to apply. In fact, even though a huge data mining
related literatures have been introduced and being applied in many fields, the actual samples
applied in housing market are very scarce. A couple of reasons for this can be cited. For one,it is often very hard for a researcher to obtain detailed information about private housing
sector, and even after successful gathering of this information, but owing to business
confidentiality there are usually limitations on opening them to the public. The other is thatthe primary focus in data mining is oriented to discover meaningful rule patterns from a
massive transaction database, which is somewhat different from our research in the context ofrelatively small contract records but massive attribute information. This causes a little3difficultly in implementation or application of data mining technology fully in real estate field.
Indeed, the content of our dataset ranges from basic demographic attribute information topurchaser’s behavioral information, which amounts to hundreds of attribute fields for eachcontractor.
As mentioned above, despite the increasing needs of market analysis of newly built
condominium there are few empirical studies in the literature, which leads marketers or
decision makers in this area to depend heavily on their experiences or intuitions. This
motivated our study to develop a rather straightforward solution to extract and summarize
unexpected or exception rule pattern from high (multiple) level of data structure space intolow (simple) level of information space through the data mining method. To put it concretely,
we propose a method to establish a good understanding of the current state of customer needs
and behavioral characteristics on the basis of exception rule mining method. We expect that a
clear and comprehensive market analysis of consumers’ needs and behavioral characteristic
in condominium can help to avoid the mismatches between supply and demand and cut down
the unsold stock of condominium.
This article is organized as follows. In section 2 we introduce the background and a review
of exception rule mining which explains the foundation of our method. In section 3, thealgorithm that we proposed is outlined and discussed. Our proposed algorithm is applied to alarge, real world condominium database in section 4. This case study is extensively described
including the behavior characteristics of condominium purchasers in our research. Finally, theconclusions are given in section 5.
2. A REVIEW OF LITERATURE
2.1 Background
It should be noted that our dataset has a relatively small contract records with massive
attribute information, and our focus is to grasp unexpected rule patterns projecting
complicated data structure into simple information space. In this sense it could be referred tosummarizing of information aimed at unexpected patterns in data structure. For this goal, we
have tried to apply a variety of methodologies from conventional statistics to the latest
neuro-computing methods. But we failed to find a meaningful result for this problem. Someof the representative methods that we have taken into consideration may be categorized into
two domains from the perspective of linearity in relation to attribute variables. One is linear
method, i.e., a conventional statistical methods represented by PCA (Principal Component
Analysis), CA(Correspondence Analysis), and MDS(Multidimensional Scaling), etc. These
traditional analytic methods are frequently termed as a data summarizing technique which isbased on the assumption of linearity among attribute variables, and would perform
excellently under ideal condition. However, when there is an increase of a vast number of
attribute variables, and their relationship does not hold from the context of linearity, it would
not operate properly. The other is as a non-linear method, including a sort of4

neuro-computing methods, like ANN (Artificial Neural Networks), SOM (Self-OrganizedMaps), etc. It is reported that these comparatively recent techniques work well compared toconventional techniques with complicated data, but understanding of training processes ofthese techniques is quite difficult and in some cases the results varied on setting parameters
and work environment (Worzala, Lenk and Silva, 1995). In either case if the number of
attribute variables increases tremendously, then it is virtually impossible to extract
summarized information.
Conventional data mining techniques are oriented to find mainly strong patterns. There is agood example why the exception rule mining is of importance instead of general data mining
in business area (Liu et al., 1999; Padmanabhan and Tuzhilin, 1999). They illustrate theusefulness of unexpectedness as a measure of interestingness in KDD (Knowledge Discovery
in Databases), and criticism about some of drawbacks in KDD by Padmanabhan and Tuzhilin(1999) can be summarized as follows:
1. It generates a very large number of rules, and most of them are obvious or irrelevant.
2. Because these obvious rules are mainly based on objective measures, such asconfidence and support, they are not so informative to marketers, thus need additional
introduction of subjective measure as unexpectedness.
3. Most of these existing algorithms are primarily data-driven and do not fully exploit
domain knowledge and intuition that managers in a business environment have.
Exception rules are those weak patterns outside the strong ones. Usually, such patterns(reliable exceptions) are unknown, unexpected, or contradictory to what the user believes.
Thus, exception rule is often beneficial since it differs from a common sense. Naturally, theterm surprise (unexpectedness) or usefulness should be argued from a “measure” point ofview. There are a good number of literatures on measure of interestingness (Freitas, 1999;
Silberschatz and Tuzhilin, 1996). This issue involves an evaluation of the patterns discovered,and generally there are two measures; an objective (data-driven) and a subjective(user-driven). Objective measure of interestingness is to capture the statistical strength of apattern and ordinarily use “confidence” and “support” for the evaluation of the discovered
patterns. On the other hand, subjective measures are related to a set of domain knowledge
given by expert, assuming that the interestingness of a pattern depends on the user and does
not only depend on the statistical strength of pattern.
In conclusion, it could be pointed out that for our problem an approach from statistics may
be mainly targeted to summarizing of whole data structure with relatively small dataset, while
an approach from data mining may be primarily oriented to discover a sort of strong patterns
with a massive dataset. Hence, for a dataset like ours with relatively small size of records,
compared to dataset of general data mining, and large size of attributes, there should be a new
approach for both summarizing data structure and extracting informative rules for marketers5in accordance to users’ demand in a given dataset. Note that in this article our concern is to
extract only unexpected rule patterns and not all information from the perspective of data
structure, leaving arguments on a comparison of statistics and data mining intact.
2.2 A review of literature related to exception rule mining
There are a few proposed exception rule mining methods in the literature. An approachbased on a syntactic comparison between a rule and a belief, labeled as ZoomUAR(Padmanabhan and Tuzhilin, 1999), based on detecting occurrences of Simpson’s paradox(Fabris and Freitas, 1999), and also there’s an approach based on contingency table presented
by Liu et al. (1999). In particular, the proposal by Liu et al. (1999) is quite similar to one
proposed in this paper. But what makes it different is that they paid attention to “deviation” ofactual frequency and expected frequency in a cell of given contingency table and identify
outstanding negative deviations as reliable exceptions. However, in this algorithm it is notclarified on how to handle “low expected frequencies” in a contingency table and how to
actually establish a point on the number of cells to be selected. We will revisit the issue oflow expected frequencies and selection criteria for significant cells in section 3.2 and 3.4
respectively.
3. PROPOSED ALGORITHM
3.1 A contingency table
One may try to grasp the relationship among attribute variables using the association
measure, and for this purpose there have been many association measures utilized. For
instance, like Pearson's product moment correlation coefficient which is a measure of thelinear association between two variables, there are a number of different correlation measureson the basis of the kinds of variables being studied. In particular, in social sciences it is very
natural that category variables are frequently employed in many circumstances. Age, forinstance, when we categorize it, classification is more general than actual age variants, like
20s, 30s, 40s, 50s, or above 60s, as the way presented in this article. Therefore, we focusedon the category (discrete) variables and a combination of two category variables or acontingency table in order to grasp the relationship across variables. For this, it is necessary
to classify attribute variables in accordance to their properties, and in the case that an attributehas continuous values, discretization is required. Figure 1(a) shows a simple example of adataset, which has four attributes: two target variables and non-target variables. An attribute
variable can be assigned to target variable or non-target variable in terms of users’ concern.
Here, a target variable indicates a variable on which a researcher focuses in terms of his/her
main interest.
Contingency tables have been used in this article to extract exception cells or significant
cells from the perspective of relationships across variables in the data. Figure 1(b) shows a
combination of attribute variables. For convenience, we put target variables in row and6

non-target variables in column in a combination table. Thus, the possible number ofcontingency tables in a combination table in Figure 1(b) is multiplication of number of target
variables and number of non-target variables. A contingency table out of a combination table
is exemplified in Figure 1(c), where r categories of a target variable are in row and c
categories of a non-target variable are in column. A contingency table, also called a crossreference table, is a table showing the number of records for each value combination of two
or more variables that constitute the table. In a two-dimensional r × c contingency tableshown in Figure 1(c), a sample of N observations is classified with respect to two category
variables; a target variable and a non-target variable. The entries in the cells in thecontingency table are frequencies. These may be transformed into proportions or percentages
but it should be noted that the data were originally frequencies or counts rather thancontinuous measurements.
Assume that a statistical model that a frequency nij in a given cell Ri C j is the observationof a probability variable Nij which follows discrete distribution, then row total and column
total are as follows.
(1)

c

r

r

c

j =1

i =1

i =1 j =1

ni + = ∑ nij , n+ j = ∑ nij , n = ∑∑ nij

Under the assumption that two category variables are independent, probability, Pij , of anobservation falling in the cell Ri C j of the table is simply the product of the marginal
probabilities, Pi + and P+ j , in the equation (2).

(2)

Pij = Pi + × P+ j (i = 1,L , r ; j = 1,L , c )Suppose that a cell where its observation exceeds far from the expected frequency N ⋅ Pij ,this implies that an event given by row and column condition is somewhat surprising or
unexpected and vice versa. Namely, if an event occurs far less than expected, it also attracts
concern or interest. Hence this sort of unexpectedness, or precisely speaking, events over- or
under- the expectation could motivate marketers/decision makers to investigate the reason of
those events. In this context, we propose an exception rule mining method using contingency
tables.
The framework of the proposed method is outlined in Figure 2. In phase 1, as a pre-process
in data mining like cleaning, selecting variables for mining is performed, and some sample
cases for this process will be given in section 4. After data preparation, for all combinational
contingency tables as shown in Figure 1(b) an extraction process of exception cells or
7

significant cells is carried out using the extraction measure. The detail process of extracting
significant cells will be argued in section 3.4. Finally, for the extracted significant cells
association rule mining method will be applied for the purpose of identifying relationships
between significant cells described in section 3.5.
3.2 Issues related to contingency tables
There are some issues which should be considered when utilizing contingency tables. The
most important question may be whether the category variables forming the contingency
table are independent or not, because the probability Pij in equation (2) is based on theassumption that two category variables are independent. For this purpose, χ 2 test is normallyutilized for the test of hypothesis of independence. When this test applied, it is generally
required to satisfy that the sample size is large and the expected frequencies are not too small,because the statistic χ 2 can be approximated to χ 2 distribution when sample size is large
enough and identical for events (Dunning, 1993). A significant overall χ 2 test for an r × ccontingency table indicates that the variables forming the table are not independent, but
provides no information as to whether the lack of independence occurs throughout the table
or only in a specific part. Whatever may be the case, for application of the proposed method,
it should be desirable that sample size is large enough and the relationship between two
category variables is not too strong.
Another issue related to contingency table is low expected frequencies. One of theassumptions made when deriving the χ 2 distribution is that the expected frequencies should
not be too small, since otherwise the assumption of χ 2 distribution would not be acceptable.
Namely, for small, sparse or skewed data the asymptotic theory may not be valid, although it
is often difficult to predict a priori whether a given data set may cause problems. There aresome arguments on how large of the expected frequencies in a cell should be (Everitt, 1992).
One of the alternatives to deal with low expected frequencies is to analyze the collapsibilityof the categories that originate in the cells with low expected frequencies, where concerning
the possible loss of information due to the aggregation of categories (Ocerin et al., 1999). In
relation to low expected frequencies, there is also a problematic issue; tables with a priori
zeros. In the case of sampling zeros the solution may be either to increase the sample size or
to add a small positive constant to each cell frequency. In many situations, however, tablesarise in which it is theoretically impossible to have observations in a cell; in this case theempty cells are usually referred to as structural zeros, and the table as a whole is incomplete.
In addition, two-dimensional contingency tables where the row and column variables havethe same number of categories occur fairly frequently in practice and are known in general as
square tables. For such tables, hypotheses relating simply to independence are not of major
importance, instead interest centers on testing for symmetry and marginal homogeneity(Everitt, 1992). In summary, regarding contingency tables, it is recommended that several
issues, as mentioned above, should be well recognized and carefully taken into consideration8

in prior.
3.3 Alternatives to Extracting of significant cells
There are several measures that could be used to extract cells in accordance to a user’s
concern in a contingency table. Some alternatives for this measure are shown in the following.
One possible measure could be the one that picks up the cells in terms of frequency ranking
order. The cells extracted by this measure might be interpreted as a strong pattern, but it has adrawback from the viewpoint that since the pattern is very strong, therefore it is not of muchinterest. At the same time, distribution of extracted cells can vary according to the way of
discretizing category variables. Another alternative could be the one that is based on theconcept of “division” between observation and expected frequency in a cell, which is similar
to one proposed by Liu et al. (1999). This alternative can be considered as a measure that isused to extract weak pattern cells or reliable exception cells. However, it is not clear on how
to actually establish a criterion on the number of cells to be selected. The last alternative inthis article is the revision of the second measure by establishing criteria of selection on thebasis of statistical test whose detail information will be given in the next subsection. It could
be cited that this alternative is the most well-grounded measure among the three proposals forour problem because it has clear statistical ground. In fact, our experiments with the dataset
using the above three measurements suggest that the last alternative is superior to the others
as exception rule mining measurement. Therefore, using the last measure we provide moredetailed procedures for extraction of significant cells from a contingency table in the next
subsection.
3.4 Detailed procedures for extraction of significant cells
As mentioned above, “difference” between actual observation and expected frequency in acell being studied can give a good ground for extracting exception rules in contingency tables.
Nevertheless because there should be more proper arguments given, consequently, thefollowing four major steps are established in order to extract significant cells in a given
contingency table. All significant cells are to be extracted throughout the following
procedures that should be conducted in all combinations of target variables and non-target
variables shown in Figure 1(b).
Step 1: Prepare an r × c contingency table
Initially, a contingency table should be made in advance, and in this time the number ofcontingency tables to be made is the number of combinations between target variables andnon-target variables. A pair of category variables in a combination table consists of a target
variable (in row) and a non-target variable (in column) as outlined in Figure 1(b). Then,calculation of ratio of frequencies and expected probabilities in each cell can be easily made.
9

Step 2: Regard distribution of a contingency table as a binomial distribution and thenapproximate it to a normal distribution
Under the assumption that two variables are independent, it is possible to regard
distribution of frequencies for each cell in the table as binomial distribution. Binomial
distributions arise commonly in statistical analysis when the data to be analyzed is derived bycounting the number of positive outcomes of repeated identical and independent experiments.
The task of counting observations in a contingency table can be cast into the form of arepeated sequence of binary trials comparing each observation in a cell with the case being
counted. These comparisons can be viewed as a sequence of binary experiments. To theextent that these assumptions of independence and stationarity are valid, we can switch to abinary distribution of cells concerning Bernoulli trials. Defining a probability variable which
conforms to a binary distribution as X ~ B( n, p) with parameters n, p (number of trials andprobability of events), then its expectation and variance are E ( X ) = np, V ( X ) = np (1 − p ) ,respectively. If n is large enough, then cumulative probability P( X ≤ x) of a binomial
distribution can be approximated into cumulative probability of a normal distribution in termsof normal function Φ in the following equation (3). if n is not very large, the probability
calculation can be improved by using the continuity correction, which considers that eachwhole number occupies the interval from 0.5 below to 0.5 above it. When an outcome x
needs to be included in the probability calculation, the normal approximation uses the intervalfrom ( x − 0.5 ) to ( x + 0.5 ).
2(3)P( X ≤ x) ≈ Φ(u ), u =u
x ± 0.5 − np
1 − t2, Φ (u ) = ∫e dt−∞
np (1 − p)2πStep 3: Remove cells with minority frequencies in the table
As stated above, small frequencies in contingency table is problematic. There are somearguments on how large frequencies should be, however it is empirically known that
normality assumptions are generally considered to hold well enough when np (1 − p ) > 5(Dunning, 1993; Everitt, 1992). The agreement between the binomial and normal
distributions is exactly what makes test statistics based on assumptions of normality so usefulin the analysis of experiments based on counting. Moreover, from the practical aspect, given
any cell with zero or near to zero frequencies, which frequently occur in real situation, allthese cells could fall into significant cells on the basis of the proposed measure. Thus, if thesesmall frequencies in a contingency table are not taken into consideration, then this could
distort real information of data structure. Consequently there is a need to exclude those cellswith scarce frequencies, as a result the proposed criterion is to calculate a theoretical number.
That is, setting the frequency on binomial distribution to zero ( x = 0) , to calculate thetheoretical probability, p , which is less than 95 % significance level given by the form as:
10

(4)

−1

f ( x) = n Cx p x (1 − p)n − x ≤ 0.05, E ( x) ≥ n(1 − 0.05n )In this paper, since the number of total observation is 786 as shown in section 4, thetheoretical number of frequency calculated by the proposed concept above is near to 3. Thus,
we set number 3 as a standpoint for our small frequencies’ problem. Certainly because the
95% significance level above to some extent is arbitrary, one could set the desirable level tosuit its purpose.
Step 4: Extract significant cells only at a given significance level
In the final step, for the cells processed from Step 1 through Step 3, it is required todetermine whether a given cell is suitable to be selected as a significant cell. For this end, we
compare the actual frequencies to theoretical frequencies processed in Step 2 and Step 3 overall cells in a contingency table using statistical significance level. Significant cells can beselectively extracted by picking up the cells in which frequencies are out of the theoreticalfrequencies calculated in Step 2 under a given statistical confidence level on normalized
distribution N(0, 1), excluding the cells which are the case of scarce frequencies in Step 3.
Specifically, we calculate the value of intensity of relationship for each significant cell by thefollowing equation (5):(5)

I = log( freq / theor )where freq is defined as actual frequency and theor is defined as theoretical frequency. For
instance, comparing two significant cells A and B as I with 10.0 and 2.0 respectively, this canbe interpreted as A is stronger than B in intensity of unexpectedness. Intuitively A may be
more surprising than B, but we believe that the intensity here should be investigated moreexhaustively. It should be noted that there are two types of significant cells; a cell surpasses
theoretical expectation (cell-over-expectation,↑) and a cell fall below theoretical
expectation(cell-under-expectation, ↓).
3.5 Significant cells according to target variables and application of association ruleminingBy arranging the extracted significant cells according to target variables, to a certain extent
latent information can be uncovered outside one’s domain knowledge. It should be noted thatthe extracted significant cells in terms of the procedures discussed in section 3.4 is nothing
but a collection of pairs or fragmented information that are regarded significant from theperspective of relationship between given target variable and non-target variable. As an
illustrative example shown in section 4, there are two pairs of significant cells: <(Sex ofhousehold: FEMALE) - (Unit type: ONE ROOM)> and <(Sex of household: FEMALE) 11

(Unit type: TWO ROOM)>. This means that if it is a female household then it is an exception
pattern that her unit type of condominium is one or two rooms. This could be interesting, but
the background or reason for this is not clear at this stage. Thus, if the relations between
significant cells or fragmented information can be investigated, comprehensive information
with consistency can be captured. In other words, the relationship among significant cells
should be systemically synthesized for consistency.
For this purpose, we pay attention to formation of significant cells sorted by target
variables. Namely, if we regard a set of significant cells, arranged by levels in a given target
variable, as a set of frequent items, then it can be interpreted that in a cluster of frequent items(significant cells) there is close relation between the items. In fact, this concept is none otherthan association rule mining, which searches for interesting relationships among items in agiven data set. This idea is motivated for market basket analysis, where the customer buying
habits are analyzed by finding associations between the different items that customers placein their shopping baskets. In general, association rules are in the form of: “Head → Body
[support, confidence]”. In this paper, we exploit “a-priori” algorithm (Agrawal and Srikant,1994), but as an alternative method to association rule for this purpose, “Rough set” method
also could be taken into consideration because both of the algorithms can be handled from theperspective of a set theory. For investigation of the relationship between significant cells
using association rule mining, the procedures and techniques for setting parameters in themining could be the same as generally conducted.
4. APPLICATION OF THE PROPOSED METHOD AND DISCUSSIONS
4.1 Data
We tested our algorithm on condominium purchasers’ contract database provided by
Recruit Co. Ltd. The data consist of 798 household records in five wards of Tokyo
metropolitan area in Japan (Shibuya, Setagaya, Ota, Shinagawa, Meguro) with over 200
attribute fields(tuples) from January 2000 to January 2001’s transactions. The attribute fields
are composed of a variety of information about demographics, behavior pattern, and theproperties they actually purchased. As stated in section 2, with the objective of applying ourapproach to the dataset, some preprocess steps should be taken in advance. Firstly, selectionof variables from the raw data, as well as designating them as target variables and non-target
variables should be carried out properly so as to grasp a good understanding of thecharacteristics of condominium households. At the same time, because all variables used in
the proposed approach should be discrete category variables, non-discrete variables should bediscretized or categorized with proper size of levels in order to avoid some possible annoying
problems discussed in section 3.2 in designing contingency tables. This issue may be referred
as a “feature selection” matter in choosing proper variables and their levels (Ocerin et al.,1999).
As for the selection of variables to be used we paid attention to the well-known key
12

questions among real estate agencies; 4W+1H “Where, Who, Why, What, How” ofcustomers’ decision on final purchase. These questions are buyer oriented in respect to how acondominium can appeal to buyers, how it can offer a viable solution to buyers’ housingneeds and wants, and how best to convey the messages. According to these criteria, weselected and categorized the variables into three main categories as shown in Table 1. There
are three main categories indicating “Where & Who”, which represents household attributes(demographics variables), “How & Why” as behavior (behavioral variables), and “What” asproperty characteristics (condominium variables). As for discretization of the selectedvariables, since most of them are originally discrete, we integrated levels of the variables intolarger levels rather than subdividing them. Because the variables are categorized too detailed
in the raw data, otherwise there would be too many zero frequencies in contingency tables.
Moreover, for each behavior variables we classified households into some meaningful
segments using cluster analysis. These segmented groups of households, or market
segmentation, which normally defined as the dividing of groups of customers intosub-markets or segments, also could be a good result of categorization. With respect tomarket segmentation, each size of segments produced by segmentation should be an
appropriate size for effectiveness and profitability of marketing strategies (Wedel andKamakura, 1998). This implies that there would not be extremely small sized segments andas a result, it could be expected to reduce low values’ frequencies in a contingency table.
Consequently, the resulting variables selected are totally 27 variables shown in Table 1.
Groups of target variables in this research are either of behavior and condominium variables,
but most of the discussions in the remainder are focused on condominium variables as target
variables. In this case, non-target variables are the rest demographics and behavior variables,
whose levels are presented in Table 2. In this article, by limiting only on behavior variables inTable 1, we will provide more detailed information about the resulting categorization andcharacteristics of each segment in the next subsection.
4.2 Behavior characteristics
Motivation of purchase
The image of his or her own choice of housing varies in terms of individual’s motivation.
For instance, there is a gap in choice between household that purchase with the reason“Because of a child growing up, (s)he should change to a larger house”, and the reason“because (s)he wants to live near the seaside leisurely”. This questionnaire was delivered tohouseholds that have purchased, and asked on their motivation of purchase based on 23
criteria with 5 at maximum. We divided households into 4 categories on the basis of cluster of
respondents; “investment (B11)”, “acquisition of ownership (B12)”, “acquisition of larger
house (B13)”, and “no particular reason (B14)”. The most frequent answer is “investment”
followed by “acquisition of ownership”.
13Concerned items as important factor
It is natural that the image of housing choice varies on what customers pursue. This
question was asked about the important criteria in the selection. In this item, we categorizedhouseholds into 5 segments on the basis of cluster of their responses; “price(B21)”,“accessibility(B22)”, “location(B23)”, “building design(B24)”, and “facilities andothers(B25)”. The most frequent answer is “price” which covers more than 70 %, followedby “accessibility”.
Search pattern
For instance, there is difference in housing image between a household that “gathers
information before visiting a model room” and a household that “visits a model room before
gathering further information”. It may be inferred that the former compares and makes
decision after full investigation, whereas the latter can be categorized as impulse buyer. Sucha difference in search patterns also seems to closely relate to the behavior of customer’s
housing choice.
Information sources for searching housing
The characteristics of customers also appeared to be closely related to where they obtain
the information from. For example, comparing two customer layers, one customer layer that
relied heavily on internet use and the other that favor of leaflet, there may appear quite
different characteristics between them. From this point of view, we sorted seven
representative information sources as: housing magazine, newspaper advertisement/leaflet,
direct mail/bulletin publication, internet, signboard or poster at showroom, salesman, andintroduced by others. We also have detailed information about what constitutes the main
information sources in housing search for a household at five steps as: the stage for1)consideration of purchase, 2)gathering information, 3)starting to visit showroom,4)deciding a purchase, and 5)contracting. Note that the main information sources used for ahousehold are different from step to step, and this requires to group households in terms ofsimilar patterns or segments. For this, the partitioning clustering method “Daisy” (Kaufman
and Rousseeuw, 1990) was employed. The result indicates that four segments are appropriate
for the task as follow: internet/poster, housing magazine, leaflet, introduced by others asshown in Table 2.
Media environment
In addition, by investigating the media environment that customer accesses, we could
figure out the characteristics of purchaser. Taking newspaper for example, there could be
different customer layers generated depending on the type of newspaper that they read. For
instance, compared the readers of economic newspapers and daily-sports, there could appear
differences in interest and behavior between them. Thus, we divided purchasers into 3
14

categories of “high, medium, or low” based on the way they access the media environment ofinternet, fax, and housing magazine.
Customer satisfaction
Generally purchasers are quite sensitive to the level of services provided, it is known that acustomer satisfied with good services may purchase a more expensive condominium. In thismanner, services provided could affect the purchase activity of a customer. The services
provided could be defined in many spectrums, but in this article, nine categories ofsatisfaction were provided in the questionnaire survey: the satisfaction of 1) explanation
about target condominium, 2) explanation about surrounding environment, 3) explanation
about financial planning, 4) explanation about purchasing procedures, 5) explanation about
corporate management of the estate/after services, 6) response to questions, 7) politeness ofreception, 8) speed of reception, and lastly 9) confidence of reception. Based on the degree ofsatisfaction of these criteria, we divided purchasers into three categories of “satisfied”,“in-between”, or “unsatisfied”.
Decisive factors or resigned factors in purchasing
These variables contain the items that were the decisive or resigned factors when acustomer makes the ultimate decision in purchasing his/her condominium. The compositionof questionnaire is the same as “Concerned items as important factor” described above. We
believe that this kind of information is important, since it provides the insight into the changeof psychology of the customer. That is what item(s) were actually more important or
unimportant at the final stage compared to his/her concerned items at the beginning stage.
The relationship between decisive and resigned factors of purchasing is to some extent
trade-off. The most frequent decisive factors in purchasing are “location”(36%),“accessibility”(23%) followed by “price”(21%) as compared to “design”(45%), “price”(14%)
and “accessibility”(12%) as resigned factors.
4.3 Extracted significant cells
In this subsection, the significant cells in a contingency table are extracted through all
combination of variables using the proposed method in section 2. First, a combination tableshown in Figure 1(b) should be designed, based upon how to designate attribute variables astarget variables and the others as non-target variables. Again in this article, the term “target
variables” indicates a group of variables on which a researcher focuses as his/her maininterest. In this work, we set a scenario; condominium variables as target variables, and theremainder demographics variables and behavioral variables as non-target variables. In thiscase, the number of contingency table combinations by a set of target variables (8 attribute
variables) and a set of non-target variables (the rest 19 attribute variables) consists of 152
pairs (8 x 19), where the total number of cells is 3,648. With this setting, we applied the15method presented in section 2. The resulting number of extracted significant cells is 240, at
99% confidence level set for suppressing emergence of too many pairs, which amounts to
6.6% of total cells. Figure 3(a) illustrates distribution of significant cells in this framework.
The prominent sets of combinations in the distribution are as follows. Firstly for the set oftarget variables, “price”(C1) and “ownership space”(C5) are high, and for the set of
non-target variables, “number of tenant”(D3), “family type”(D4), “annual income ofHH”(D11), and “decisive factor of purchasing”(B7) are high. In particular, as seen in Figure3(a), there exist pairs like “price(C1) - annual income of HH(D11)”, “ownership space(C5) annual income of HH(D11)” and “ownership space(C5) - number of tenant(D3)” that showprominent frequencies. This implies that in these pairs there are more exceptional patterns.
Again note that the significant cells were extracted on the basis of two optional cases, namely
either too much or too less frequency in a cell compared to its theoretical expectation.
After arranging the significant cells in terms of each level of target variables as a pivot,
illustrated in Table 3(a), then we tried to summarize a series of exception rule patterns. The
arranged set of significant cells is a collection of exception pairs, which are identified ascharacteristics of non-target variables related to target variables. Our resulting interpretationof them suggests that some of the combinations are quite contrary to our beliefs in the light ofthe domain knowledge. Some of these examples will be given in next subsection. Moreover,there seem to be repetitive pattern of apparently related pairs of significant cells.
4.4 Synthesis of significant cells
As mentioned in section 2, in order to grasp a comprehensive relation of the significant
cells, we need to reorganize the significant cells or the fragmented information and apply
association rule mining. In other words, relationship between the significant cells should be
investigated systematically for consistent interpretation. In our framework, as in association
rule mining, which is often labeled a basket analysis, transaction records of customers areanalogous to each level of target variables, and items are correspondence to significant cells.
Note that because the cells extracted from a contingency table are in one of the cases, either
cell-over-expectation or cell-under-expectation, it is convenient to identify them in terms of
unique code for each item. Any definition for the coding would be fine as long as the code isunique. For an intuitive example, the code “2C3↑” means that “2” as a variable ID, “3” as alevel of the variable, and “↑” as the cell-over-expectation.
With the prepared codes exemplified in Table 3(b), we performed association rule mining
and as a result, we obtained 1,819 association rules under the threshold support (5%) andconfidence (100%), which are too many to grasp for meaningful information. The main
reason that such a good number of rules emerge, despite 100% confidence, could be
attributed to the fact that there are too many partially duplicated or nested rules. Thus, taking
the nested rules away, we tried to summarize them by devising an intuitive but effective
technique mainly aimed at our result from association rule.
16

The proposed technique is also based on a contingency table. An association rule is usually
represented by X → Y with support (s) and confidence (c), where X and Y are a set of items.
In our result of association rule, most of the items in both head part(X) and body part (Y) aresingle items, such as the form “(16C5H),(11C2H),(10.000% 100.00%)” which represents（head)(body)(support, confidence). Thus, assume that a contingency table composed ofrows with a set of all unique items which occur in head part, columns with a set of all uniqueitems in body part, then we can put a rule from association rule into the specified cell inaccordance to a pair which can be decomposed into head (row) and body (column). Once
built a contingency table, each frequency in the table can be interpreted as an integrated
measure of support and confidence, where rows represent items of head part and columns
represent items of body part. Considering the same example: “(16C5↑),(11C2↑)”, byallocating the head (16C5↑) representing “Residential area:60s ㎡↑” in a row and the body
(11C2↑) representing “Age of HH:「MALE」↓” in a column, we can easily count thefrequency in the cell where two items crossed. Nevertheless, it should be noted that in thistechnique there can be three possible alternatives depending on the number of items in a heador a body; i.e. ①(r1) → (c1), ②(r1, r2,…,ri) → (c1), ③(r1, r2,…,ri) → (c1,c2,…,cj),
where ri, cj items in row(head), items in column(body). Except alternative ① above, theother two alternatives could be unreasonable to be applied for the proposed technique because
if there are more than two items in either head or body, it is unnatural to decompose items inthe same manner.
Nevertheless, again for our task to outline result of association rule, the proposed techniqueperforms successfully since most items are in the case of alternative ①, even though there
are some cases that items fall into the alternative ② or ③. The resulted contingency table,
as a summary of association rule, is illustrated in Figure 3(b). Due to the limitations of space,
we omit the detailed result, but the result of association rule is sufficient to give clue to agood understanding of the relation between significant cells and it has closed to perfectconsistency and easily understandable. Some of the association rule results are displayed inTable 4. The rule samples shown are in higher rank, and there are many pairs where the headof household is female. Note the case of line 2 in the table, “Sex of HH:「MALE」↓→ Sex of
HH:「FEMALE」↑“, which represents low probability of male household or high probabilityof a female household, namely a female household, a typical example of trade-off relation.
Tracing these pairs, we can depict the characteristics of the female household as exception
rules like this:
They have low income of average annual salaries in the range of 4~6 million Yen, andprobably single families. Therefore, they are looking for a relatively small residential unit
with one or two bedrooms. For them, “price” and “building facilities” are prone to be
decisive factors of purchase.
In the similar fashion, we present some other interesting rules found are followed:17

-

If access time to the nearest railway station to be over 10 minutes, then “accessibility”
as a resigned factors become increased, which implies that potential accessibility inmind would be around 10 minutes on walk or within about 350 meters from railway
station.

-If occupancy space is around 60~70 ㎡ or 2~3 bed rooms which is typical and popular,then the prominent items seem to be “investment” as a motivation of purchase and“price” as a decisive factor. Moreover, in this case, “introduction and others” as
information sources for searching housing is extremely scarce.

-

Households with average annual salary of 15~20 million Yen are targeted to high-risebuilding especially over 16 floors, and in this case “building facilities” is prone to be adecisive factor of purchase. In particular, the possibility that their occupations to be“financial” is very high.

Again, note that these are exception rules, though it seems general or strong patterns, theserules are surpassing or falling short of theoretical expectation. Besides this analysis withcondominium variables as target variables, we also carried out the same method to behavior
variables as target variables, which also provides quite informative results relating tobehavioral characteristics of purchasers explained by the demographics and condominium
variables.
4.5 A validity of the proposed method
In this subsection we try to investigate the validity of the proposed method. Because it is
not easy to find any methods oriented to our problem raised in section 1. Therefore, as a
substitute, we compare some results produced by the proposed method to those from thehistogram of the descriptive statistics. For simplicity, we take some extracted significant cells
in Table 4 and some of their histograms in Figure 4, and in both cases, single family type isset as a criterion.
To begin with, distribution of variable “Annual income of HH” in Figure 4(a) is quiteconsistent with the first 5 lines in Table 4, where annual income of about 70 % of single
families is fewer than 8 million Yen. The distribution of “Occupation of HH” in Figure 4(d) isalso quite intelligible, because the occupation of “MANAGER” is quite rare in the case of
single family. But, note that in the case of “Decisive factor of purchasing” in Figure 4(b) and“Media environment to access” in Figure 4(c), distribution of each variable disagrees to therules in the Table 4, because “LOCATION” as decisive factor of purchasing is not so rarecase and “LOW” as media environment to access is not frequent case, which contradicts theitems in table 4. After all, as shown in this example, it could be pointed out that the structureof exception rules/patterns is not self-evident under statistical distributions, and in some cases
it contradicts the distributions as shown in Figure 4 because these distributions are strong
patterns.
18

5. CONCLUSION
In this work we have presented a rather straightforward method of extracting exception
rules from a dataset with large attributes and applied the method in order to describe
condominium purchasers’ behavior. The method consists of two major steps: first, we focus
on the contingency table showing the responses of subjects to one variable as a function ofanother variable as “association measure”. We extract the cell(s) in the given statisticalsignificance level, and summarize them on the basis of target variables. At this time thesubjective evaluation of “exception” can be interpreted objectively by the significance level.
Second, because these extracted cells or significant cells are fragment information, theyshould be synthesized systematically for consistency. For this, we utilize association rulemining in order to synthesize the fragment information as “pattern bundle in a basket”. The
rule patterns and their relationship by the proposed method are excellent in extracting andinterpreting of exception rules because the rules are not only easily understandable but also
consistent, and therefore it is expected to have high potential of application for a dataset withmassive attributes.
For future work, different thresholds on association rule for significant cells can beexperimented and a variety of results can be compared. For the reliability and validity of the
proposed method, datasets with different characteristics and sizes can be evaluated andcompared to the domain knowledge of marketers.
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Agrawal, Rakesh and Ramakrishnan Srikant (1994), "Fast algorithms for mining association
rules," In Proceedings of the 20th International Conference on Very Large Data Bases,
Santiago, Chile.
Bounsaythip, Catherine and Esa Rinta-Runsala (2001), "Overview of Data Mining for
Customer Behavior Modeling," VTT Information Technology Research Report
TTE1-2001-18.
Dunning, Ted (1993), "Accurate Methods for the Statistics of Surprise and Coincidence,"
Computational Linguistics, 19(1), 61-74.
Everitt, BS (1992), The analysis of contingency tables. 2d ed. London:Chapman and Hall.
Fabris, Carem C. and Alex A. Freitas (1999), "Discovering surprising patterns by detecting
occurrences of Simpson's paradox," Research and Development in Intelligent Systems
XVI, 148-160.
Freitas, Alex A. (1999), "On Rule Interestingness Measures," Knowledge-Based Systems,
Vol.12, 309-315
Kaufman, Leonard and Peter J. Rousseeuw (1990), Finding Groups in Data: An Introduction
to Cluster Analysis. New York:Wiley.
Liu, Huan, Hongjun Lu, Ling Feng and Farhad Hussain (1999), "Efficient search of reliable
19exceptions," PAKDD(Pacific-Asia Conference on Knowledge Discovery and Data
Mining), 194-203.
Ocerin, J.M. Caridad, R. Espejo Mohedano, A. Gallego Segador, (1999), “Automatic
aggregation of categories in multivariate contingency tables using information theory,”
Computational Statistics & Data Analysis, 29, 285-294.
Padmanabhan, Balaji and Alexander Tuzhilin (1999), "Unexpectedness as a measure ofinterestingness in knowledge discovery," Decision Support Systems, 27(3), 303-318.
Silberschatz, Abraham and Alexander, Tuzhilin (1996), "What Makes Patterns Interesting in
Knowledge Discovery Systems," IEEE Transactions of Knowledge and Data
Engineering, 8(6), 970-974.
Wedel, Michel and Wagner A. Kamakura (1998), Market segmentation: Concepts andmethodological foundations. Boston: Kluwer Academic Publishers.
Worzala, Elaine, Lenk, Margarita and Silva, Ana (1995), "An Exploration of Neural
Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research,
Vol.10, No.2, 185-201.

20

Table 1
SELECTED VARIABLES
Demographics Variables

Behavior Variables

Type of addresschange(before/after)
D2. Experience of residence
purchase
D3. Number of tenant
D4. Family type of household
D5. Means of transportation forwork of HH
D6. Age of HH
D7. Sex of HH
D8. Type of employment for HH
D9. Occupation of HH
D10. Category of business for HH
D11. Annual income of HH

B1. Motivation of purchasing
B2. Important factor of purchasing
B3. Pattern of gathering information
B4. Main mass media of purchasing
B5. Media environment to access
B6. Customer satisfaction
B7. Decisive factor of purchasing
B8. Resigned factor of purchasing

D1.

※

Condominium Variables
C1. Price
C2. Time to walk fromnearest railway station
C3. Ward
C4. Unit type
C5. Ownership space
C6. Number of room
C7. Total number of floors
C8. Total number of houses

HH : Head of a household

21

Table 2
LEVELS OF CATEGORY VARIABLES
Variables(Unit)
D1
D2
D3(Number)D41within a ward
60.5%(483)2
outside award
39.5%(315)3levels4

for the firsttime
77.8%(620)1
0.5%(4)

replace
15.1%(120)increase
7.2%(57)2
21.8%(174)single21.6%(171)

couple32.4%(257)

railway
85.7%(663)
20s
9.9%(79)male
79.4%(632)business
24%(182)
construction
7.5%(57)car
4.8%(37)30s
54.4%(433)female20.6%(164)self-employed/specialist
6.6%(52)
service22%(167)
manufacture
18%(137)3
35.5%(283)
couple＋child
40.7%(322)bus
2.2%(17)40s
25.5%(203)～4
3.5%(28)
investment
40.1%(319)priceindependent
24.4%(195)4
23.4%(187)

5

6

7

5
16.2%(129)62.4%(19)7
0.3%(2)8others
5.3%(42)
walk/cycle
7.4%(57)50s
7.5%(60)

over 60s
2.6%(21)student/others
3.3%(26)
engineer
16.4%(124)finance
13.7%(104)specialist
15.6%(118)
broadcast
9.1%(69)

manager
22%(167)
commerce
15.4%(117)IT
11.6%(88)others
24.8%(189)～623.2%(184)～825.3%(201)～1018.1%(144)～12
14.7%(117)～15
9.8%(78)～20
3.3%(26)

20～
2%(16)

larger house
30.3%(241)others
9.4%(75)

internet/poster
12%(96)high(internet・
fax・JJ）
23.8%(190)satisfied
38.8%(309)ownership
20.2%(161)
pricedependent
75.6%(603)double(long-term)
30.7%(234)housingmagazine
36.2%(289)low(no internet）
12.7%(101)in-between
53.1%(423)

B7

price
21.4%(169)

accessibility
23.2%(183)area
37.1%(293)

design
11.5%(91)"

B8

price
14.7%(108)accessibility
13.1%(96)area
13.5%(99)design
49.2%(361)～20
0.8%(6)～30
4.9%(39)～40
19.9%(157)～50
29.9%(236)～60
23.6%(186)～70
11.3%(89)～80
5.5%(43)80～
4.1%(32)～528.1%(196)
Shibuya
18.8%(150)1R
13.7%(109)～30
1.8%(14)R1
10.7%(85)～3 F
8.8%(70)～30
23.6%(188)～10
40%(279)
Setagaya
25.9%(207)2R
32.2%(256)～40
2.9%(23)R2
25.2%(200)～5 F
18.1%(144)～50
26.1%(208)～15
26.5%(185)Ota
27.4%(219)3R
48.2%(383)～50
4%(31)
R3
55%(437)～10 F
53%(421)～100
29.8%(238)～20
4.7%(33)
Shinagawa
18.2%(145)4R+
5.8%(46)～60
11.6%(91)R4+
9.1%(72)～15 F
17.3%(137)～200
15%(120)20～
0.6%(4)
Meguro9.6%(77)～70
23%(180)～80
34.2%(268)～90
14.9%(117)90～
7.7%(60)～20 F
1.4%(11)200～
5.5%(44)～30 F
1.4%(11)

D5
D6
D7
D8
D9
D10
D11
(Million
Yen）
B1
B2
B3
B4
B5
B6

C1
(Million
Yen）
C2(Minutes)
C3
C4
C5
(㎡)
C6
C7
C8

businessman
90.2%(715)

standard
35.3%(269)double(short-term)
22%(168)
newspaper
leaflet43.6%(348)impulse
11.9%(91)
introduction
8.1%(65)

medium
63.5%(507)unsatisfied
8.2%(65)

building
facility
6.8%(54)
building
facility
9.5%(70)※ JJ: monthly housing magazine for rent and purchase, D1~11 : Demographics, B1~7 : Behavior, C1~7 : Condominium

22

Table 3
SAMPLE OF ARRANGEMENT OF EXTRACTED SIGNIFICANT CELLS(a) Sample of Actual Significant Cells(b) Sample of Coding for Association Rule
MiningC1-1 : Price:「Under 2 million Yen」 --------------

C1-1 : 2C3↑,4C1↑,11C8↑,18C5↑,18C1↑

Experience of residence purchase:「INCREASE」 8.284

C1-2 : 3C2↑,4C1↑,5C3↑,7C2↑,7C1↓,10C1↑,

Family type of household:「SINGLE」 3.049

11C1↑,11C2↑,18C1↑

Annual income of HH:「Over 20 million Yen」 31.4

C1-3 : 3C2↑,4C1↑,5C4↑,6C1↑,7C2↑,7C1↓,

Decisive factor of purchasing:「BUILDING FACILITY」

9C5↓,9C3↑,10C3↓,11C5↓,11C6↓,…

8.83

C1-4 : 6C3↓,8C2↓,11C6↓,11C3↑

Decisive factor of purchasing:「PRICE」 3.077

C1-5 : 3C2↓,4C1↓,7C2↓,11C2↓,11C5↑,11C4↑,
18C1↓,18C5↑

C1-2 : Price:「2~3 million Yen」--------------------…

…
※

The figure indicates the value of logarithm ofratio that actual frequency divided by
expectation probability.

23※

nCk : n stands for a variable, k stands
for the level of k .
↑: cell-over-expectation,↓: cell-under-expectation.

Table 4
AN EXAMPLE OF RESULT FROM ASSOCIATIONRULE MINING(In the case of Family type:「SINGLE」↑)
Annual income of HH:「Under 4 Million Yen」↑
Annual income of HH:「4～6 Million Yen」↑
Annual income of HH:「8～10 Million Yen」↓
Annual income of HH:「10～12 Million Yen」↓
Annual income of HH:「12～15 Million Yen」↓
Sex of HH:「MALE」↓
Sex of HH:「FEMALE」↑
Number of tenant:「1」↑Number of tenant:「3」↓Number of tenant:「4」↓Occupation of HH:「MANAGER」↓
Family type:「COUPLE + CHILD」↓
Decisive factor of purchasing:「PRICE」↑
Decisive factor of purchasing:「AREA」↓
Media environment to access:「LOW」↑※↑：cell-over-expectation, ↓：cell-under-expectation

24

Figure 1
CONTINGENCY TABLES IN THE PROPOSED METHOD(a) Example of a Dataset
Target(b) Combination Table(c) Contingency Table

Non-target

variablevariable

T1

N1

T2

N1 N2

N2

1

3

3

4

4

2

1

2

3

4

1

2

2

5

2

1

3

1

2

2

2

3

4

3

T1

…

Nn

…

C c sum

C1

C2

R1

n11

n12 … n1c

Non-target Vars.

n1+

TargetT2

R2

n21 n22 … n2 c n2+

Vars.

…

…

…

…

…

Tt

Rr

nr1 nr 2 … nrc

nr +

sum

n+1 n+ 2 … n+c

n

25

…

…

Figure 2
EXCEPTION RULE MINING FRAMEWORK
Condominium contractor database←Selection & filtering

Phase 1

Selection of attribute variables← Discretization
Composition of contingency tables← Extraction measure

Phase 2Extraction of significant cells← Association rule
Phase 3Synthesis of significant cells

26

Figure 3
EXTRACTED SIGNIFICANT CELLS(a) Distribution of Significant Cells(Target Variables : Condominium Variables)(b) Relation Table Among Significant
Cells By Association Rule

※ Row : Head, Column : Body

27

Figure 4
HISTOGRAM OF ATTRIBUTE VARIABLES IN THE CASE OF FAMILY TYPE : SINGLE

28

Appendix
Table A-1
A SAMPLE OF ASSOCIATION RULES ACROSS EXTRACTED SIGNIFICANT CELLS
Number of frequency： 84
Sex of HH:「MALE」↓⇒
Annual income of HH:「400～600 million Yen」↑
Sex of HH:「MALE」↓⇒
Sex of HH:「FEMALE」↑
Sex of HH:「MALE」↓⇒
Number of tenant:「1」↑Sex of HH:「MALE」↓⇒
Family type:「SINGLE」↑
Number of frequency： 82
Sex of HH:「FEMALE」↑⇒
Number of tenant:「1」↑Sex of HH:「FEMALE」↑⇒
Family type:「SINGLE」↑
Number of frequency： 81
Annual income of HH:「400～600 million Yen」↑ ⇒Sex of HH:「FEMALE」↑
Annual income of HH:「400～600 million Yen」↑ ⇒Number of tenant:「1」↑Annual income of HH:「400～600 million Yen」↑ ⇒Family type:「SINGLE」↑
Number of frequency： 79
Number of tenant:「1」↑ ⇒Family type:「SINGLE」↑
Number of frequency： 74
Number of tenant:「1」↑ ⇒Sex of HH:「FEMALE」↑
Family type:「SINGLE」↑ ⇒Number of tenant:「1」↑Number of frequency： 70
Family type:「SINGLE」↑ ⇒Sex of HH:「FEMALE」↑
Number of frequency： 69
Sex of HH:「FEMALE」↑⇒
Annual income of HH:「400～600 million Yen」↑
Number of frequency： 64
Number of tenant:「1」↑ ⇒Annual income of HH:「400～600 million Yen」↑
Number of frequency： 60
Family type:「SINGLE」↑ ⇒Annual income of HH:「400～600 million Yen」↑
Annual income of HH:「400～600 million Yen」↑ ⇒Sex of HH:「MALE」↓
Number of frequency： 53
Number of tenant:「1」↑ ⇒Sex of HH:「MALE」↓
Sex of HH:「FEMALE」↑⇒
Sex of HH:「MALE」↓
Number of frequency： 49
Family type:「SINGLE」↑ ⇒Sex of HH:「MALE」↓
Number of frequency： 43
Annual income of HH:「800～1,000 million Yen」↓⇒Annual income of HH:「400～600 million Yen」↑
Annual income of HH:「800～1,000 million Yen」↓⇒
Sex of HH:「MALE」↓
Annual income of HH:「800～1,000 million Yen」↓⇒
Sex of HH:「FEMALE」↑
Annual income of HH:「800～1,000 million Yen」↓⇒
Number of tenant:「1」↑Annual income of HH:「800～1,000 million Yen」↓⇒
Family type:「SINGLE」↑
Ownership space:「30㎡ ZONE」↑⇒
Annual income of HH:「400～600 million Yen」↑
Ownership space:「30㎡ ZONE」↑⇒
Sex of HH:「MALE」↓
Ownership space:「30㎡ ZONE」↑⇒
Sex of HH:「FEMALE」↑
Ownership space:「30㎡ ZONE」↑⇒
Number of tenant:「1」↑Ownership space:「30㎡ ZONE」↑⇒
Family type:「SINGLE」↑
Number of frequency： 35
Annual income of HH:「400 under million Yen」↑⇒
Sex of HH:「FEMALE」↑
Annual income of HH:「400 under million Yen」↑⇒
Number of tenant:「1」↑Annual income of HH:「400 under million Yen」↑⇒
Family type:「SINGLE」↑
Number of frequency： 34
Total number of floor:「3F Under」↑⇒
Annual income of HH:「400～600 million Yen」↑
Total number of floor:「3F Under」↑⇒
Family type:「SINGLE」↑
Number of frequency： 30
Total number of floor:「3F Under」↑⇒
Sex of HH:「MALE」↓
Total number of floor:「3F Under」↑⇒
Sex of HH:「FEMALE」↑
Total number of floor:「3F Under」↑⇒
Number of tenant:「1」↑Number of frequency： 27
Number of tenant:「1」↓ ⇒Family type:「SINGLE」↓
Family type:「SINGLE」↓ ⇒Number of tenant:「1」↓…

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