Bulletinof
The Japanese Classification Society

Volume 2
December 1988
JAPAN

Contents
Recent Developments in Quantitative Analysis of Japanese Documents
Masakatsu Murakami
Analysis of the Growth Data according to Age on Boys and Girls--An aspect of the Growth Pattern of Height-Reiko Ninomiya, Yukiko Higuchi, Shikiko Hayakawa
Presence of subtypes of anxiety neurosis as revealed by classifying its symptoms
by the Mini-numerical Taxonomy System
Yuko Suzuki, Koujirou Shukutani, Shizuo Aizawa, Shinobu Tatsunami, and Nagasumi
Yago, Takao Ishizuka
Evaluation of River Water Quality of a Mountains Ecosystem by Cluster Analysis
Tomoyuki Hakamata, Tatemasa Hirata and Kohji Muraoka
Toward AGenuine Cladistics
Kuniyoshi Ohta, Seiroku Sakai
AVisual Data Analysis System
Masahiro Mizuta
Application of the Graphic Software GDAS for Classification Problems (1)Kiyoshi Katayama, Tugihiro Shu, Keiji Yajima
Application of the Graphic Software GDAS for Classification Problems (2)Kiyoshi Katayama, Tugihiro Shu, Keiji Yajima

1 of 12JCS Bulletin Vol. 2

Recent Developments in Quantitative Analysis
of Japanese Documents
Masakatsu Murakami
The Institute of Statistical Mathematics
4-6-7, Minami-Azabu, Minato-ku
Tokyo Japan

Since the development of computer technology has enabled us to analyze Japanesecharacters such as Kanji and Kana, the quantitative analysis of Japanese documents has come toa new stage.
Some data bases of Japanese literature are already constructred in the Data Processing
Center of Kyoto University and Institute of Investigations on Japanese Literary Documents;
however, the analytical methods for using these data bases have not been developed.
Since 1974, we have constructed a data base for Nichiren's documents to solve thequestions of authorship of five disputed documents. Nichiren (1222~1282) opend a newschool of buddism called Nichiren-shu in the Kamakura era.
The purpose of this paper is to introduce a method of quantitative analysis using Nichiren's
papers and to point out the difficulties of the quantitative analysis of Japanese documents.
The papers we use for the quantitative analysis are as follows.
Nichiren's papers,
Forged papers,
Papers of Nichiren's students,
Disputed papers,

24
16
5
5

(85000 words)(15000 words)(13000 words)(20000 words)

Using these papers, we try to distinguish the characteristic feratures of Nichiren's
sentences. We focus on the following two different types of information for each paper.
1) The frequency of all the words.
2) The information about a literary style such as the distribution of sentence length, thedistribution of word length, the mean of sentence length, the mean of word length, the typetoken ratio, the ratio of parts of speech, Yule's K-statistics, etc.
In addition to our quantitative analysis of Nichiren's papers, we also discuss the difficulties
of analysis of Japanese documents.

2 of 12JCS Bulletin Vol. 2Analysis of the Growth Data
According to Age on Boys and Girls- An Aspect of the Growth Pattern of Height Reiko Ninomiya
Computational Laboratory
Japan Women's University
Yukiko Higuchi
Shikiko Hayakawa
Faculty of Home Economics
Japan Women's University
1-2-8, Mejirodai, Bunkyo-ku
Tokyo JapanAs a result of our research on body form classification of boys and girls and it's variationin growth of adolecent, we grasped variation of body form and individual difference ofgrowing-up speed were remarkable.
In this report to analyze individual difference of growth, we used the data of personal
growth by age.
We classified growth patterns. Concerning the age of the maximum growth, we discussed
classified patterns, growing-up amount in adolecence term, height of first grader in elementary
school and growing-up amount of the age of the maximum growth.
The results are as follows:
1) The classified patterns were characterized by the age of the maximum growth, level ofgrowing-up speed and difference of growing-up speed by age.
2) There were significant differences between growing-up amount of the age of the maximum
growth.
3) The growing-up amount in adolecent term were significantly greater for boys and girls
whose age of the maximum growth were later than for boys and girls whose age of the
maximum growth were earlier.
4) The height of first grader in elementary school were significantly smaller for boys whose ageof the maximum growth were later than for boys whose age of the maximum growth were
earlier.

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Presence of Subtypes of Anxiety Neurosis as Revealedby Classifying its Symptoms
by the Mini-numerical Taxonomy System
Yuko Suzuki, Koujirou Shukutani,
Shizuo Aizawa, Shinobu Tatsunami*,and Nagasumi Yago*
Department of Neuropsychiatory and *Radioisotope Research Institute,
St. Marianna University School of Medicine,
Sugao 2-16-1, Miyamae-ku, Kawasaki-shi 203
Japan

Number of serious cases of anxiety neurosis have increased in recent years in Japan.
Symptoms shown by these patients may not be improved either by drug administration or byphychiatric therapy. For the purpose of finding out possible factors that underline thesymptoms, we have constructed and analyzed a data file on personalities of 55 patients ofanxiety neurosis including 14 patients of the serious cases. The raw data which were collectedby means of the University of Tokyo Personality Inventory (abbreviated as TPI) were firsttransformed into personality scores according to the handling manual of TPI. Then, the file
was analyzed by the Mini-Numerical Taxonomy System (abbreviated as MINTS) which has
been developed by Dr. Ohsumi at the National Institute of Statistical Mathematics, Tokyo.
Using the group average method, we could classify the patients on the dendrogram into 10
clusters which were numbered from 1 through 10. Of the 10 clusters, Clusters 1, 2 and 3
clearly composed the major group while Clusters 4, 5 and 7 the other. A characteristic featureof the major group was that the patients showed good prognosis. Patients that belonged to thesecond group did not show any improvements by pscychiatric treatments. These patients were
found either hysterical or epileptical. In conclusion, the cluster analysis of symptoms by the
MINTS was found to be quite useful in diagnozing patients of anxiety neurosis especially in
preliminary classification of the patients into several subtypes.

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Patient Classification
for
Medical Resource Allocation
Takao Ishizuka
Faculty of Business Management
Asia University
24-10 Sakai, 5-chome, Musashino-shi
Tokyo Japan

Medical resources should be allocated according to needs of patients. Amethodtoclassify
patients into some groups on the standpoint of resource allocation is called “casemixclassification.” Patient classification by the case mix studymightbeveryimportantforthe
hospitals from now on
The objective of this paper is to summarize several patient classification systems including
DRG which is adopted for Medicare Prospective Payment System in U.S.A., and propose agraphic case mix analysis method by posterior probability of some factor affecting the variationof length of stay
It is demonstrated that this method is quite useful as:1) quantization of the extent of separation of mixed distribution,2) visualization of case mix complexity, and3) optimal selection of variables for classifying cases into homogeneous groups fromthe statistical and clinical view.

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Evaluation of River Water Quality of a Mountains Ecosystem
by Cluster Analysis
Tomoyuki Hakamata, Tatemasa Hirata and Kohji Muraoka
Water and Soil Environment Division
The National Institute for Environmental Studies
Tsukuba-shi, Ibaraki 305
Japan

River water is sink of cycling materials in an area ecosystem, source of which isprecipitation. Because the river water quality reflects a lot of biogeochemical phenomena, theland use effects in the ecosystem may be assessed by exploring the river water quality. In thisstudy, cluster analysis was used to evaluate the river water quality in a mountains ecosystem
which have many typical land-use, e.g., forest, paddy field, upland field, orchard, village, etc.
The 153 sampling sites of the rivers were selected in the Tsukuba mountains ecosystem.
The sample water was collected in late winter or spring of 1986. The land use types of the sites
were recorded at the same time.
A frequency distribution of silica(SiO2) was symmetrical. However, that of pH, elctric
conductance(EC) and other elements was asymmetrical, the smaller class having much higher
frequency. The medians of the data were compared with those of Kanto district reported by
Kobayashi(1960). The medians of nitrate nitrogen(NO3) and chlorine obtained from thisecosystem were higher than or nearly equal to those of Kanto district. Nitrate nitrogen is
assumed to be increased by biological processes in forests and agricultural land of theecosystem. Chlorine is assumed to be moved into the ecosystem from the Pacific Ocean placedin 40km eastern part of Mt. Tsukuba.
The sampling sites were classified into 10 groups by the Ward method of cluster analysis
from the viewpoint of water quality and land-use. The groups is reflected degree or pattern of
human impacts (Table 1). Phosphate(PO4), nitrite nitrogen(NO2) and NO3 are concerned withhuman life. High EC value is often observed in water near which menactivelymove.SiO2 isconnected with quarrying characteristic in this region.

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Table 1. The classification of sampling sites from the viewpoint of water quality
and land-use-----------------------------------------------------------------------------------------------------------------------------------------------------Groups
Numbers ofCharacteristics of water quality

Location or land-use
samples------------------------------------------------------------------------------------------------------------------------------------------------------

(1)

12

(2)

11

(3)

11

(4)

12

(5)

21

(6)

12

(7)
(8)
(9)

13
10
7

(10)

2

Low pH, lowest SiO2 & middle
NO3
Lower EC/NO3 & higher SiO2
than (1)Higher PO4 SiO2 than (1) & (2)mainly forest
Higher EC & lower PO4/SiO2
than (3) & near road
Lower EC/PO4/NO2 than(3) & (4), middle pH/SiO2/NO3
Lower pH & higher EC/NO2
High NO2/NO3
High NO2/NO3 & low pH/SiO2
Highest SiO2, high pH/EC& low NO2/NO3
High SiO2, low or middle others

Near mountain top & forest
Upper mountain & fore
Lower mountain &Upper outside of village
Mountain & forest
Near village or near mainroad
Lower outsides of village
Upper parts of main river
Village or under quarry
Lower outsides of large
village or lower parts ofmain river------------------------------------------------------------------------------------------------------------------------------------------------------

Some typical cases in which natural riverbeds concserve water quality were observed: forexample, in R. Kabaho three samples from a branch with artificial riverbeds (ground sill) wereclassified in the group (5), whereas three samples from another branch with natural riverbeds
were classified in the group (1). Natural riverbed should be protected even when forests
around the river are used for any purposes.

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Toward a Genuine Cladistics
Kuniyoshi Ohta
Seiroku Sakai
Daito Bunka University
Higashi-Matsuyama, 355
Saitama-ken, Japan

Systematic biology is one of the oldest among the sciences of classification (e.g. Knight,
1981; Hachiuma, 1987). This paper, based upon Ohta's (1988) study on the exact history ofsystematics theory, gives a brief account of the main current schools of systematic biology. It
is pointed out that Smirnov's (1968) “exact systematics” should be clearly discriminated fromSokal and Sneath's(1963) “numerical taxonomy”, because the former is not concerned withoverall similarity.
It has lately become the fashion, at least among the German- and English-speaking
systematists, to advocate the Hennigian cladism (Hennig, 1966). The second part of this paper
criticizes cladism or Hennigism in the following points (Ohta, 1988)(1) The Hennigian or cladist regards W. Hennig as the founder of phylogenetic systematics andof cladistics. This is, however, completely wrong. The founders of phylogenetic systematics
are Darwin, Haeckel, and others, and the founder of cladistics is P. C. Mitchell (1901, 1905).
(2) The Hennigian regards systematics, phylogenetics, and cladism as all equivalent, but this isincorrect.
(3) Hennigism or cladism is not true cladistics.
(4) “Hennig's principle” of identification of sister relationships by means of synapomorphy isinaccurate in words and logic. It should be replaced by what we call Mitchell's Principle ofidentification of most relatives by means of shared, uniquely derived characters.
(5) Hennig's classification and definition of ”monophyly”, “paraphyly”, and “polyphyly” arefundamentally errneous. There is not a third alternative to monophyly and polyphyly in the true
sense--logical and historical--of the terms defined by Haeckel.
(6) A monophyletic group in the true sense is a phyletic group such that a global root species(G) is included in it.
(7) Hennigian's “monophyletic group” seems to be our hologroup, but Hennigian's thesis that
only their “monophyletic groups” are taxa is a narrow approach. We recognize semiphyletic aswell as holophyletic groups as taxa.
(8) Hennigian's ideology of dismissing ancestor-descendant relationship is wrong.
The third part of the present paper, based on Ohta's(1988) work, gives a set of rigorous
definitions and measures of four components of phylogenetic relationship which has been so
far very vague in systematic biology.
(i) Phylogenetic relationship is composed of generalogical and genetic relationships, andthe former composed of cladistic and chronistic relationships.
(ii) The cladistic relationship in the strict sense between any two species in a phylogenetic
tree is defined by the triplet (k, l;m), where k and l are respectively the species-generation
numbers between each of the two species and their local root species (L), and m is the speciesgeneration number between L and G. Evidently m is a measure of predivergence kinshipbetween the two species, while the vector (k,l), or the scaler (k+l), might be useful for
representing a measure of postdivergence kinship between the two species. For example, twosister species in the true sense corresponds to the relationship in the (1,1)th degree.
8 of 12JCS Bulletin Vol. 2

(iii) The chronistic relationship between two species is defined as the triplet (r,s;t), where r
and s are respectively the year numbers between each of the two species and L, and t is the year
number between L and G.
(iv) Genetic relationship is defined as the degree of genotypic homology between two
species.
The last part of this paper offers a probablistic illustration of Mitchell's Principle for
identification of closest relatives. Consider a simplest, irreversable transformation series, A0,
A1, where A0 and A1 are respectively the primitive and derived characters, and thetransformation probability is assumed to be α in unit time. Then the probability that two
species in the relationship of (r,s;t) have in common the derived character A1 is
t

t

r

s

P (A1 r, s; t ) = 1–(1–α ) +(1–α ) [1–(1–α ) ][1–(1–α ) ],where the first and second terms represent the probability of homology, and the last term
represents the probability of homoplasy. On the other hand, the probability that the same two
species have in common the primitive character A0 is
r+s +tP (A1 r, s; t ) = (1–α )Here, we define the information contents of shared, derived characters and of shared,
primitive characters respectively as
I (A1 r, s; t ) = –log P (A1 r, s; t ),I (A0 r, s; t ) = –log P (A0 r, s; t ).
Then it will be easily shown that the information content of shared, derived characters will
increase as α ﾕ 0, and vice versa for shared primitive characters. This is an informationtheoretic foundation of Mitchell's Principle.
Furthermore, we shall consider the problem of estimating the genealogical relationship byusing shared, uniquely derived characters. For the purpose, let P(t  A1) be the probability thatthe degree of relatedness of two species sharing A1 is t, and R(t) be the probability of t in any
two species within a phylogenetic tree. Then we have

P (t A1 ) =P (A1 t ) R (t )
Σ P (A1 w) R (w)w

and if we can assume that R(t) is a uniform distribution and that A1 is a uniquely derived
character, we obtain a very simple theorem.
t

t

r

s

P (A1 r, s; t ) = 1–(1–α ) +(1–α ) [1–(1–α ) ][1–(1–α ) ],This is a probablistic foundation of Mitchell's Principle. The final part of the present paper
points out that there is a strong need for an Integrated Taxonomy (Sakai, 1980) or Synthetic
Systematics (Ohta, 1988) to resolve the endless controversy among the current systematic
schools and to advance a new framework in theoretical systematics.
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References(1) Hachiuma, T. (1987) The Dawn of Theoretical Classification Science (In Japanese), Takeda
Shoten, Fujisawa.
(2) Hennig, W. (1966) Phylogenetic Systematics, Univ. of Illinois Press, Urbana.
(3) Knight, D. (1981) Ordering the World, Burnett Books, London.
(4) Mitchell, P.C. (1901) Trans. Linn. Soc. Lond. Zool., 8: 173-275.
(5) Mitchell, P.C. (1905) Trans. Zool. Soc. Lond., 17: 437-536.
(6) Ohta, K. (1988) In: Zool. Soc. Jap. (ed.) Evolution--the New Synthesis, Ch. II (inJapanese), Gakkai Shuppan Center, Tokyo.
(7) Sakai, S. (1980) Insect Integrated Taxonomy, Daito Bunka Univ. Takasaka.
(8) Smirnov, E.S. (1968) Taksonomicheskii analiz, Izd. Moskov. Univ. Moskva.
(9) Sokal, R.R. and P.H.A. Sneath (1963) Principles of Numerical Taxonomy, Freeman, San
Francisco.

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A Visual Data Analysis System
Masahiro Mizuta
Division of Information Engineering
Hokkaido University
North 13, West 8, Kita-ku, Sapporo-shi
Hokkaido-060, JapanThe efficient manipulations of data analysis system have begun to arouse considerable
attention with the popularization of the systems. The menu method and the command method
are ordinary manipulations of them. Another methods are developed with the advance of thestudy of man-machine interface. One of the most attractive method is the utilization ofgraphics, in other words vision. Window system and ICON of work stations or Macintosh are
typical examples of them. Visual programming environments have been studied in the field of
the information engineering.
The conception of the visual programming is applied to the field of the data analysis and we
get a view of visual data analysis. Some of the methods of data analysis use graphics, which
are called graphical methods. The objective of the paper is to visualize the manipulations ofdata analysis. We produced a visual data analysis system on a micro by way of trial. An
outline of the system is mentioned in the following.
Any procedure of data analysis, for example, read from a file, computation of variances or
PCA etc., is considered as an ICON abstractly. The ICON have some INPUTs and some
OUTPUTs. It has also some OPTIONs i.e. parameters, which specify the action of theprocedure. In the case of cluster analysis, the ICON has an INPUT, an OUTPUT and an
OPTION. The INPUT implies similarity matrix, the OUTPUT is a set of labels of objects, the
OPTION specifies the method of cluster analysis and parameters. The sequences of data
analysis can be expressed the sequences of the ICONs. The trial product of a visual data
analysis system is depended on this iconic model.
Anormal manipulation of the system will be explained here.
(1) Click the place of 'ICONS' with mouse. The list of ICONs which is registered,appear immediately. Select an ICON with mouse.
(2) Place the ICON on the Work Area on the screen. Repeat step 1 and 2.
(3) Connect an OUTPUT of an ICON to an INPUT of another ICON by lines.
(4) If necessary, set OPTIONs of ICONs.
(5) Execute each ICON in order, according to click 'EXEC' on the ICON with mouse.
(6) Change the ICONs or the connection of the ICONs on the screen, for trial and error.
(7) Terminate the system by QUIT.
One of the advantages of the system is flexibility of the procedures correspond to ICON.
The registration of the procedures is easy, because each procedure is independent program.
The body of the system communicates with the procedures by two ways. The first is theutilization of the specific file and the second is of the argument list. The program of aprocedure can get the informations from the body of the system with only one of these ways.
A construction of a data analysis system with plain mainpulation is not plain. However, theuse of visual is very hopeful for easy operation of the system. We report one system which is
produced with the intention of furnishing a field of programs.

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Application of the Graphic Software GDAS
for Classification Problems
Kiyoshi Katayama, Tugihiro Shu
Keiji Yajima
Institute of JUSE
4-30-3, Sendagaya, Shibuya-ku
Tokyo, Japan

Newly developed software product GDAS(Graphical Data Analysis System) aims to carryout data analysis by means of graphical presentation on the microcomputer. There have been acouple of graph drawing program products and the GDAS is characterized by strong orientation
toward data analysis through fair drawings.
To illustrate the classification capability some branches such as so-called radar chart,
diagonal presentation of three variables(%), and the time series plotting are shortly introduced.
For example, in radar chart utilizing a scale adjustment facility a comparative study of thesamples may be provided.Feature of computer display is characterized by easily tackled trialsof
a number drawings, with the size alteration and the sample batch changes.
In the time when there is an overflowing data supply, one ought to put the software in fulluse of the classification techniques.

12 of 12JCS Bulletin Vol. 2