Visual Exploration of a Series of Academic Conferences
Kazuo Misue
Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba 305-8573, Japan
Email:misue@cs.tsukuba.ac.jp
http://www.cs.tsukuba.ac.jp/˜misue/

Abstract—The trends in a research field, especially changes
in the features over the years, are subjects of interest for
many researchers. This paper reports an exploratory analysisof the changes of research topics in an academic field. The
target data of the analysis are the author-keywords includedin papers presented at a series of academic conferences,
IEEE International Conference on Data Mining (ICDM). The
analysis process consists of three phases: (1) frequency of
keywords, (2) appearance of keywords in papers, and (3)relationships among keywords. In phase 1, bar charts wereused to observe the ranking of frequencies. In phases 2 and 3,
anchored maps were adopted. The anchored maps are basedon the spring-embedder model, but they provide viewpoints byusing fixed “anchors.” The analysis process revealed the major
topics in the field of data mining and some changes in therelationships among topics.

technique based on a fundamental graph-layout method,
the spring-embedder model, but provides viewpoints, i.e.,anchors, that facilitate the observation of changes in the
networks.
The contributions of this paper can be listed as follows:
1) Contributing to knowledge about changes in research
fields related to data mining,2) Understanding the extraction of graph structures fromdata of papers, and
3) Using anchored maps in exploratory analyses.

Keywords-information visualization;
graph drawing; academic conferences

Many attempts have been made over the years to draw
maps representing the topics of documents. Lin [1] proposeda method to visualize document space by using Kohonen’s
feature map. The method visually depicts a collection of
documents on information retrieval. Wise et al. [2] presented
ThemeScape, a visualization technique that provides 3Dlandscapes of topics and themes in the document corpora. Fujimura et al. [3] proposed a variation of largescale tag clouds. Their method displays a landscape of
relationships among tags (topics) of many documents. Friedand Kobourov [4] demonstrated an approach for the visual
exploration of research papers.
Many researchers have attempted to visualize networks ofauthors and papers. Chen and Paul [5] aimed to visualize intellectual structures in a knowledge domain. They visualized
author co-citation networks to represent knowledge landscapes. Elmqvist and Tsigas [6] presented CiteWiz, a platform for bibliographic visualization that provides the visualization of keyword and co-authorship networks. PerianesRodrı́guez et al. [7] proposed a method for detecting,identifying and visualizing research groups in co-authorshipnetworks, which they visualized by using the Kamada-Kawai
algorithm, a variation of the spring-embedder model. By
focusing on relationships between authors and papers, abipartite network can be obtained, for which Misue [8]presented a visual analysis tool that uses anchored maps tovisualize networks involving academic papers and authors.
Naud et al. [9] and Ito et al. [10] presented methods tovisualize a bipartite network spherically in 3D space.

visualexploration;

I. I NTRODUCTION
The trends in a research field, especially changes in thefeatures over the years are subjects of interest for many
researchers. This paper reports an exploratory analysis of thechanges of research topics in an academic field. Hypotheses
are not prepared; instead, it was our aim to explore the data
to discover interesting features.
Researchers unfamiliar with a field of research may findinformation about the features of the research field useful for
improve their understanding of the field. Researchers who
have been active in the field for many years may be familiar
with such features. Nevertheless, these researchers may still
benefit from a reconfirmation, especially if they previously
overlooked a feature of their field of research.
The research involved an analysis of keywords that wereused by the authors of papers presented at a series ofacademic conferences, IEEE International Conference on
Data Mining (ICDM). Initially, we focused on the frequencies at which keywords appeared. However, frequencies
do not provide useful information about the relationships
between keywords. Our focus therefore was on networks
representing the relationships between papers and keywords,
and additionally on the relationships between keywords
and the changes they undergo over the years. This was
accomplished by adopting anchored maps to observe the
networks. An anchored map is an information visualization

II. R ELATED W ORK
A. Research Map

B. Anchored Map
An anchored map [11] is a node-link diagram basedon the spring-embedder model [12]. Anchored maps wereoriginally designed for drawing bipartite graphs (two-mode
networks). The technique fixes the nodes in one of the sets,called “anchors,” at predetermined positions on a circumference, while allowing the nodes in the other set, called“free nodes,” to be arranged freely. Any graph can also be
drawn as an anchored map by dividing the nodes into twosets, provided that edges with both of their endpoints in thesame set are ignored. More specifically, an anchored map
was visualized using two steps:
1)
Arrange the anchors on the circumference at equalintervals prior to deciding the ordering of theanchors on the circumference.
2)
Fix the anchors and use the spring embedder toposition the free nodes such that they appropriately
express their relationships to the anchors.
The number of edge crossings is strongly influenced
by how the anchors are ordered. Deciding the ordering istherefore the most critical problem. The tool offers numericindices as substitutes for the aesthetic criteria of graphdrawing. Our work involved the use of the sum of the
distances between every free node and the center of thecircumferences, referred to as the eccentricity of the nodes,
as an index.

Figure 1.

Frequencies of top 20 keywords.

III. TARGET DATA
Data was obtained from papers that were accepted as
regular papers for the ICDM between 2002 and 2013, withthe condition that a paper had to contain one or more
keywords. For example, a paper that was presented at ICDM
2012 contained six keywords: differential privacy, histogram,
lossy compression, Fourier transform, and clustering.
The occurrence of orthographical variants required us
to take some preparatory steps, including the conversionof all keywords to lowercase and the unification of somehyphenated words, for example, “time series” and “timeseries”, to remove variants. Singular and plural forms were
not unified.
In our analysis, every regular paper was assigned an ID,
which was used together with the year of publication andthe keywords that were listed by the author. Our analysis did
not use the paper title, abstract, author names, or affiliations.
IV. F REQUENCY OF K EYWORDS
The total number of keywords that were found was 3058,
and the number of unique keywords was 2048. The mean of
the frequency of appearance of keywords was 1.49. There
were 341 keywords that appeared more than once and 1707
keywords that appeared only once, although these numbers
would depend on processing for orthographical variants.
Next, the frequency at which each keyword appeared
between 2002 and 2013 was determined. Figure 1 displays

Figure 2.

100% stacked bars of top 20 keywords.

a stacked bar chart representing the frequencies of the top
20 keywords. The total length of each bar represents the
cumulative total of 12 years. In the most of ordinal stackedbar charts, layers are presented in significantly different
colors to enhance the visibility of the borders of the layers. In
Figure 1, however, we assigned a color along the gradationfrom red to green to each layer, because the layers represent
years of ordinal scale. This caused the borders between layers to become unclear. We used another gradation consistingof white and a vivid color to clarify the borders.
During the 12 years under consideration, the keyword thatwas used most frequently was “clustering.” It was followedby “classification” and “data mining” in the second and third
places, respectively, although these two keywords were usedmuch less frequently than “clustering”. The frequency of useof “data mining” was about half that of “clustering.” Thefourth-most used keyword was “graph mining”, followed by“time series” and “feature selection” in the fifth place and“anomaly detection” in the seventh place, etc.
Figure 2 presents the same data as a 100% stacked bar

chart, as this was considered to be a more suitable way ofcomparing the annual distribution of frequencies.
Two of the keywords, “clustering” and “classification,”
appeared with constant frequency. Against them, the frequency of the use of “data mining” was not constant; it was
found to be low between 2006 and 2009. Although it is notpossible to obtain more detailed information from Figure 2,
many other keywords1 including “data mining” were used
between 2006 and 2009. This means that “data mining” was
not used on its own. About half of the appearances of the
keywords “graph mining” and “feature selection” were inpapers that were published during the last three years underconsideration; hence, it can be said that these two keywords
have recently become more popular.
V. A PPEARANCE OF K EYWORDS IN PAPERS
It would be possible to guess which topics were popularand to guess which topics were rising and declining in
popularity by observing the changes in the appearance
frequencies of keywords. However, this would not permit
an understanding of the relationships between the topics.
Instead, anchored maps were drawn to represent the relationships between keywords and papers. First, the top
six keywords were selected from the ranking shown in
Figure 1. The selected keywords were drawn as anchorsin the anchored maps. There is no inevitability to thenumber six and the top 10 or top 20 could also have beenchosen. However, too many anchors tend to cause unreadable
diagrams. The keyword ranking in Figure 1 shows thatthe top three keywords were significantly frequent. One
reasonable approach would have been to choose the top three
keywords, but three keywords were considered to be too few
to serve as observational targets; thus, six keywords wereconsequently chosen. Bipartite graphs were extracted that
represent the relationships between these six keywords andthose papers that contained at least one of these keywords
at least once every three years. Figure 3 shows the anchoredmaps of the four bipartite graphs. The six keywords weredrawn as anchors and the papers were drawn as free nodes.
The year in which each paper appeared is represented by acolor as in Figure 1.
2002-2004: Figure 3(a) reminds us of three major topics: “clustering,” “data mining,” and “classification.” Moreover, a few papers contained pairs of keywords such as{“clustering” and “feature selection”}, {“clustering” and“data mining”}, and {“data mining” and “classification”}.
On the other hand, “graph mining” was isolated.
2005-2007: As shown in Figure 3(b), “graph mining”
continued to be isolated. Additionally, “classification” is also
isolated. The number of papers using “classification” as a
1 E.g., data mining application, spatio-temporal data mining, high performance data mining, multimedia data mining, relational data mining,
distributed data mining, temporal data mining, stream data mining, privacypreserving data mining, etc.

keyword was found to have increased, but these papers did
not use any of the other top six keywords. The keyword“feature selection” is connected to “clustering” as they
appeared together in a paper. Some of the other papers thatused “feature selection” did not use any of the other top six
keywords.
2008-2010: In Figure 3(c) it can be seen that “classification” is connected to “time series”, although “graphmining” continues to be isolated. Additionally, the use of“feature selection” is also isolated.
2011-2013: In Figure 3(d) it is shown that a paper
connecting “clustering” and “feature selection” has been
revived. The keyword “graph mining” is also connected toanother keyword, “clustering”, for the first time. The keywords “clustering” and “classification” are also connectedto each other for the first time. On the other hand, “datamining” is isolated. The figure seems to indicate a recent
upsurge in the use of “feature selection.”
Figures 3(a)–(d) all display changes of connectivity between the top six keywords. However, there are many papers
containing the top six keywords in which two or more keywords are connected. Therefore, the changes displayed bythese bipartite graphs would not provide enough information
to allow a discussion of the features of the research field.
VI. R ELATIONSHIPS BETWEEN K EYWORDS
As there are 2048 different keywords, it is importantto also consider those other than the top six. Keyword
relationships were studied by using the top six keywords asseeds. First of all, a graph was constructed with the nodes
representing keywords and the edges representing keywords
that co-occurred in the same paper. Next, a subset of nodesconsisting of the top six keywords and all the keywords
appearing alongside these keywords was selected. Using thissubset of nodes, an induced sub-graph was then drawn in theform of anchored maps (Figure 5) in which the six keywords
were drawn as anchors and the remaining keywords as freenodes.
The period during which each keyword was in use isrepresented by a different color. The first year of each period
is represented by a hue of color as in Figure 1, while the lastyear of the period is represented by saturation. For example,
a keyword that first appeared in 2002 was assigned the color
red, and if it was still used in 2013, the most vivid red was
assigned. If it was not used after 2002, it was assigned apale red color. The color map that was used is shown in
Figure 4. Colors marked with “x” were not used.
2002-2004: Figure 5(a) shows that there were many
keywords that were used in conjunction with “clustering.”
Even in this figure, “feature selection” is connected with“clustering” as shown in Figure 3(a). In Figure 5(a) it canbe seen that the keyword “graph mining” is not isolated,for example, there are some paths that connect “graph(a) 2002–2004(b) 2005–2007(c) 2008–2010(d) 2011–2013

Figure 3.

Anchored maps representing relationships between keywords and papers.

(a) 2002–2004(b) 2005–2007(c) 2008–2010(d) 2011–2013

Figure 5.

Anchored maps representing relationships between keywords.

To 02
From
02

03

04

05

06

07

08

09

10

11

12

13

03

the global structure of the keyword network was found tohave changed and changes in last three years of this period
are more prominent than in previous years.

04
05
06
07
08
09
10
11
12
13

Figure 4. Color map for representing the terms during which keywords
were used.

mining” with “clustering.” Some keywords appear to be
hubs, for example, “scalability,” “text mining,” and “xml”,
although the low saturation of the “xms” means it is nolonger used. Other keywords such as “visualization,” “neuralnetwork,” and “self-organizing map” connect the keywords“clustering” and “data mining.”
2005-2007: In Figure 5(b) it is shown that “feature
selection” is connected not only with “clustering” but alsowith “time series.” Moreover, the number of keywords thatwere only connected with “feature selection” increased.
These keywords did not exist in 2002-2004 (in Figure 5(a)).
This might mean that “feature selection” had grown as anindependent topic. A hub-like keyword “text mining” exists
in the figure. However, both “xml” and “scalability” seemto have disappeared.
2008-2010: In Figure 5(c), the keywords “feature selection” and “graph mining” are isolated. Keywords connecting“data mining” and “classification” have also disappeared.
The keyword “bayesian” seems to be a pivot connecting“clustering” and “classification.”
2011-2013: In Figure 5(d) drastic changes can beseen in the structure over this period. Keywords around“clustering” have been greatly revived. In addition, thosearound “feature selection” have also been reactivated. New
keywords have also appeared around “graph mining” andhave made connections with other keywords. The keyword“social networks” appears to be a pivot of some connections.
Keywords such as “anomaly detection,” “topic modeling,”and “transfer learning” seem to be functioning as hubs.
On the other hand, “data mining” appears to be becoming
isolated, with some new keywords around it, but withconnections to other keywords becoming thin.
Throughout Figure 5(a)–(d), new keywords can be seen
to be born. Over the period of 12 years under consideration

VII. D ISCUSSION
The process described above involves an exploratory
analysis of data from academic papers. No hypotheses wereprepared, but some trends were found in the data. Bar charts
are useful to present the ranking of frequencies. As shownin Figure 1, a stacked bar chart was used to understandchanges in frequencies. However, it is difficult to readchanges in rankings from this chart. An attempt was made tounderstand relations by using network structures that wereextracted from the data, because a series of anchored maps
representing changes in the network structures obviously
provides more information than bar charts.
The figures in this paper seem to provide an overview ofa series of academic conferences; however, these figures do
not reveal everything, as it is not possible to estimate information excluded from the figures. Figure 3 only includes
papers containing one of the top six keywords, while otherpapers were excluded. This means that more than a few
papers were excluded and that the feature changes causedby these papers cannot be presupposed. Future work willattempt to increase the number of seed keywords, i.e., thenumber of anchors with the aim of comparing the featuresthat can be read from anchored maps. Figure 5 only focusedon seed keywords and the keywords adjacent to them. In
the future, we will focus on increasing the number of seeds
and/or enlarging the set of target keywords by finding otherrelationship landscapes among keywords.
VIII. C ONCLUSION
This paper reports an exploratory analysis of the research field of data mining. Keywords that were provided
by authors of regular papers for a series of international
conferences, the ICDM, were used as target data. First,
the main research areas were identified by representing
the appearance frequencies of keywords in the form of
a bar chart. In this way three major topics were found:“clustering,” “data mining,” and “classification.” Next, thechanges in the frequency of each keyword were investigated,for example, the keywords “graph mining” and “feature
selection” were found to have increased in popularity during
the last years forming part of the study.
Moreover, graphs were extracted by using relationships
between papers and keywords. This enabled us to develop
an understanding of the features of the relationships among
keywords by observing anchored maps of the extracted
graphs. Our analysis only included the top six keywords;
therefore, we would have to study other variations as well.
Future work will include testing the validity of the featuresthat were obtained from the anchored maps.

ACKNOWLEDGMENT
We thank Professor Shusaku Tsumoto, Shimane University, for providing the data from the ICDM papers and theopportunity to analyze them.
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