Visualization of Corpus Data by a Dual Hierarchical Data
Visualization Technique
Takayuki Itoh Haruho Tachibana
Graduate School of Humanities and Sciences, Ochanomizu University
2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan
itot at is.ocha.ac.jp
Abstract
The paper presents a technique for visualizationof corpus data consists of thousands of Japanesenewspaper articles, and introduces several interesting trends discovered from the results. The
technique first generates keyword-document matrices from the newspaper corpus, and respectively applies hierarchical clustering for rows andcolumns of the matrices. It then displays the two
sets of clusters applying our own dual hierarchical data visualization technique. The visualization
technique provides a mechanism to interact thetwo visualization components each other, so thatusers can freely explore the detail of the corpus
data. This paper first describes the algorithm of
the dual hierarchical data visualization technique,and then introduces our implementation and experiments of the visualization of the newspaper
corpus data.
Keywords: Visualization, Clustering, Newspaper
Corpus, Hierarchical Data, Matrix Data.

1

play space usage by converting such sparse matrix
data into node-link data such as tree or graph.
It can drastically save the display space usage inmany cases, because it represents only n or m data
items. Matrix-oriented and node-oriented visualization techniques have their own bottlenecks, andsome technical papers compare the readability ofvisualization results between the techniques [7].

This paper presents a new node-oriented visualization technique for such sparse matrices.
The technique ﬁrst generates clusters of rows andcolumns of the matrices. It then generates two
hierarchical data from the clusters, and visualizes those using our own dual hierarchical datavisualization technique. Also, our implementation provides a mechanism to interact the dual visualization components each other, so that users
can explore rows and columns alternately. The
mechanism is especially useful to realize detail-ondemand access of the matrix data.

Introduction

Matrix data is a very common data in ourdaily life, and therefore matrix data visualization
technique is an active research topic. Keyworddocument matrix data is a typical matrix data
used for various computer science ﬁelds, which collect importance values of selected keywords in aset of documents. The matrix data can be usedfor visualization of document data [15].
In many cases matrix data in our daily life isvery sparse, and it is not always reasonable if we
represent such sparse data using matrix-oriented
visualization techniques. For example, if we haven × m matrix and values of 90% of the data items
are zero, we may feel the matrix-oriented representation looks redundant. Many past visualization studies have attempted to save the dis-

We applied the dual hierarchical data visualization technique to the visualization of Japanesenewspaper corpus data. Figure 1 shows the procedure of our implementation for the visualization of
the corpus data, and its snapshot. Our implementation ﬁrst extracts articles from the corpus, andcalculates importance values of selected keywords
for each article. It then constructs keyword-article
matrix collecting the importance values, and visualizes the matrix data. We believe the technique
can contribute so that users can freely explore andaccess to the interested parts of the large corpus.
The paper proves the eﬀectiveness of our technique for the newspaper corpus, by introducing
several interesting trends discovered from the visualization results.

Documents
K
e
y
w
o
rd
s

r1
r2
…
rn

c1 c2

…

cm

a11 a12a21 a22…
an1 an2

…
…

a1m
a2m

…

anm

Clicking keywords, its importance is indicated by colors in the right side

Keywords

Documents

Vector representationof columns
c1=(a11, …, an1)…
cm =(a1m, …, anm)

Clusters of columns
c2,c3

c1,cn

Vector representationof rows
r1=(a11, …, a1m)…
rn=(an1, …, anm)

Clusters of rows
r2,r 3

r1,rn

Height: sum of importance
Clicking documents, body of the document is indicated, and sum of importancefor the cluster of clicked document is indicated by colors in the left side

Figure 1. Overview of the newspaper corpus visualization technique applying our own dual
hierarchical data visualization technique. (Left) The technique first generates clusters ofcolumns and rows of keyword-document matrix data. (Right) The technique then visualizes
the two sets of clusters applying the dual hierarchical data visualization technique.

2

Related Work

2.1 Hierarchical Data Visualization
The technique presented in this paper utilizes
our hierarchical data visualization method presented in [9, 10]. This method represents a hierarchy as nested rectangles, and leaf-nodes as painted
icons. The visualization technique places thousands of leaf-nodes into one display space whilesatisfying the following conditions:• It does not overlap the leaf-nodes and branchnodes in a single hierarchy of other nodes.
• It attempts to minimize the display area requirement.
• It draws all leaf-nodes by equally shaped andsized icons.
• It attempts to minimize aspect ratio and areaof rectangular subspaces.
One of the features of our hierarchical data visualization technique is all-in-one visualization oflower-level data items, rather than representationand navigation of hierarchy between parents andchildren nodes. Many of well-known hierarchicaldata visualization techniques can be categorized
as node-link and space-ﬁlling approaches. Spaceﬁlling visualization technique is an approach to
represent all lower-level data items in one display
space as well as our own technique. Quantum

Treemap [4] archives to represent leaf-nodes as
equally shaped and sized. Target of the Quantum
Treemap is very similar to our own technique, andactually Quantum Treemap can be an alternativeof our technique for the purpose of this paper.
Experiments described in [9] discusses trade-oﬀs
between Quantum Treemap and our technique,where our technique had better results in aspectratio of subregions and stability of layout among
similar hierarchical data.
As well as our technique, recent Treemap also
proposes hierarchy visualization from tabular data[5]. Their approach diﬀers from ours since it clusters rows according to various attributes, sinceours independently clusters columns and rows
and provides user interface to interact them eachother.

2.2 Matrix Data Visualization
Visualization of matrix data is an active research topic. Some early excellent works for visualization of tables, matrices, and spreadsheets,
focused on exploration and navigation of the data[13]. Matrix data visualization can be often applied as an alternative of node-link data visualization for graphs or networks [3]. This approach often improves occlusion problems of graph/network
visualization techniques. However, display usageof the approach is not very eﬃcient if the input
data is not dense.
Matrix zoom [1] uses both matrix- and node-based visualization techniques, which provides
overview of huge matrix and detailed view of subgraphs; however, they did not apply the node-link
view to simultaneous representation of columns
and rows as the presented technique focuses.

in the left window, and a graph of keywords inthe right window, and provides a graphical userinterface to interlock the left and right part of thedisplay each other. However, it is diﬃcult to display hundreds of documents and keywords in one
display space without scroll operation.

2.3 Multivariate Data Visualization

3
The presented technique treats columns androws of matrix data as multivariate items. Recent parallel coordinates techniques [11] focus oncluster visualization of multivariate data, but we
did not apply such techniques because it seems
diﬃcult to provide clickable user interface for arbitrary columns or rows. Principal component projection is also often used to visually discover clusters [12], but again, it is often diﬃcult to makearbitrary data item clickable, because many data
items may overlap each other on a display space.
Another approach for multivariate data is using
glyphs [6]. Current implementation of the technique presented in this paper simply represents
attributes of data items by heights, colors, andshapes of bars, but more various representations
can be realized by applying glyphs.

2.4 Document Data Visualization
Document data is one of the typical applications of information visualization. Manyof presented document data visualization techniques target representation of temporal thematicchange, for discovery of time-varying patterns andtrends of documents. ThemeRiver [8] visualizes
temporal change of frequency of speciﬁc keywords
applying a visual metaphor of a river, for visual
analysis and discovery of temporal thematic patterns and trends. Wong et al. [17] presented atechnique to visualize frequent sequential patterns
from time series dataset of documents.
Many of other techniques focus on visualization of correlations among large number of documents, where our technique also focuses on thispoint. Galaxies [16] represents multi-dimensionalfeature values of documents as a 2D scatterplot
map. InfoSky [2] plots documents in hierarchicallystructured spaces. These techniques well works torepresent distances among documents or clustersof documents: however, the visualization results
may be very sparse. Since we would like to use thedocument data visualization technique as graphical user interface, it is desirable that metaphors of
documents are easily clickable. That is one reason
why the technique presented in this paper applies
our own hierarchical data visualization technique.
DualNAVI [15] is one of the techniques mostanalogous to ours. It displays a list of documents

Dual Hierarchical Data Visualization

The paper presents a new technique to visualize matrix data by applying dual hierarchical datavisualization technique. Features of the presented
technique are as follows:
Feature 1) The technique represents the hierarchical structures of rows and columns, rather thaneach of multivariate values of rows and columns.
Feature 2) The technique represents clusteringresults of rows and columns as hierarchical data,
and aims to display all rows and columns in one
display as clickable icons, rather than representing hierarchy relationship of parent and children
nodes of the hierarchical data.
Feature 3) The technique represents the rows
and columns as equally-sized small icons, because
it saves the display area, and fairly represents thewhole parts of the data.
Feature 4) The technique targets matrix data
which have hundreds or thousands of rows andcolumns. We think these are appropriate data
sizes for the technique, since it focuses on providing user interface so that users can easily clickeach of the icons of rows and columns.

3.1 Clustering of Rows and Columns
Let us describe the data items of matrix data
as follows: columns c1 to cm , rows r1 to rn ,and element values a11 to anm , as shown in Figure 1. Here the technique treats columns C =(c1 , .., cm ) as n-dimensional vectors, such as ci =(a1i , .., ani ). Similarly, the technique treats rows
R = (r1 , .., rn ) as m-dimensional vectors, such as
rj = (aj1 , .., ajm ). Value type of aij can be binary,
integer, real, or any others which clustering algorithms can be applied. Example shown in Section
4 uses real values.
The technique generates clusters of columns,
applying agglomerative clustering method according to Euclidian distances, where non-hierarchicalclustering methods (i.e. Self organizing map,k-means method) can be also applied.
Our
implementation simply generates combination ofcolumns by iteratively coupling the columns orgroups of columns according to their similarity values, and then generates nested clusters
by grouping the columns according user-deﬁnedthreshold values. Then, the process is also applied
to rows as well as columns. Finally the techniquegenerates two sets of nested clusters, and they canbe treated as two hierarchical data.

3.2 Visualization of Clusters

be conﬁgured according to application-oriented semantics. As described in Section 3.3, interaction
between the left and right parts can be also conﬁgured according to application-semantics. We modiﬁed visual attributes and interaction mechanism
between the left and right parts to customize for
corpus data visualization as follows:

The technique then visualizes the two hierarchical data. Let the visualization module for rows
as the left part, and the visualization module forcolumns as the right part. Therefore, the leftpart visualizes n rows r1 to rn , and the right partvisualizes m columns c1 to cm . They represent
columns and rows as three dimensional bar charts,and nested clusters as nested rectangular borders.
Here, colors, shapes, and heights of the bars vary
according to application-oriented semantics.

• Heights of icons in the left part are proporm
tional to the sum of importance, j=1 cij fori-th keyword.
• Hues of icons in the left part represent thesum of importance in a speciﬁc cluster of documents, where redness denotes that importance is high, and blueness denotes that importance is low.
• A user can choice one of the conditions toﬁlter the articles while calculating the sum ofimportance of the keywords.

3.3 Interaction between the Dual Hierarchical Data

• When a cursor points one of the icons of keywords, it indicates the keyword.

Our technique provides two-way interaction between the left and right parts, so that users caninteractively explore the data items. When users
click a row in the left part, then the techniquehighlights columns in the right part, which are
related to the clicked row in the left part. Similarly, when users click a column in the right part,
then the technique highlights rows in the left part,which are related to the clicked column in the rightpart.
Suppose that a user clicks ri in the leftpart. The technique calculates visual attributesof cj using aij , according to user-deﬁned conditions. The conditions can be deﬁned according toapplication-oriented semantics, and an example isdescribed in Section 4. Consequently, the technique highlights interesting columns in the rightpart.
Similarly, when a user clicks cj in the right part,the technique calculates visual attributes of rows,
and highlights interesting rows in the left part.

4• When a user inputs a keyword, it highlights
the icon, which corresponds to the keyword,in the left part.
• When a user clicks an icon or a cluster in theleft part, it indicates a list of keywords inside
the cluster.
• When a user clicks an icon of keyword in theleft part, the right part then represents theimportance of the keyword for each document
by R of RGB values.
• When a user clicks another icon of keyword
in the left part, the right part then represents
the importance of the keyword for each document by G of RGB values.
• When a user clicks an icon in the right part,it indicates the body of the document.

Corpus Data Visualization

Let keywords be r1 to rn , where n is the number of keywords. Also, let documents be c1 to
cm , where m is the number of documents. Our
implementation clusters the documents and keywords as described in Section 3.1, where aij isthe importance of i-th keyword in the j-th document. It then displays the clustered keywords inthe left part, and the clustered documents in theright part.
As described in Section 3.2, visual attributesof nodes (i.e. shapes, colors, and heights) can• When a user clicks an icon or a cluster in theright part, the left part then calculates thehue of icons of keywords. The hue is calculated from the sum of importance of the keywords in the documents of the clicked cluster.

5

Experiments

We implemented the dual hierarchical data visualization technique on Java 1.5, and tested on
IBM ThinkPad X60 (CPU 1.8GHz, RAM 1GB)with Windows XP.

5.1 Matrix Data Generation from a JapaneseNewspaper Corpus
We used a corpus of Japanese Mainichi newspaper in 1998 and 1999, where articles are stored
in XML format containing date, headline, body,and additional annotations. We extracted articles
which have the keyword ”business information”
in their annotations, where 2178 articles are extracted in 1998, and 1400 articles are extracted in
1999. We then calculated importance of words foreach document, and extracted top 200 words in1998 and 1999 respectively, according to the sumof importance values. We applied ”Chasen”, an
open software for morpheme analysis of Japanese
documents, and ”termex”, also an open software
for importance calculation of words in Japanese
documents 1 . We looked over the 200 words andmanually selected 150 words in 1998 and 1999 respectively; here we preferentially selected name ofcompanies, name of items, technical and ﬁnancial
terms, because we thought such words would bringtrends of business information. Finally, we generated keyword-article matrices in 1998 and 1999
respectively. Figure 2 illustrates the above processes.

5.2 Visualization Results
We generated two hierarchical data from thematrix generated by the above process. Remarkthat the examples shown in this section applied
only one threshold for clustering, and therefore
the visualization results are not nested.
Figure 3 is an example of visualization of thenewspaper articles in 1998. Here we clicked a cluster in the left part (indicated by a circle [A]), andlooked a list of keywords in the cluster. We clicked
the two keywords ”USA” and ”Internet” in thelist, and therefore several icons in the right partwere highlighted. Here, icons corresponding to articles about Internet were highlighted in red, andicons corresponding to articles about USA werehighlighted in green. Intensities of the highlighted
icons denoted the importance of these keywords.
From this visualization result, we observed two
clusters (indicated by circles [B] and [C]) that
there were yellow icons inside. We clicked thecluster indicated by the circle [B] in Figure 3(upper), and observed that several bars were thenhighlighted in non-blue colors in the left part, asshown in Figure 3(upper). The highlighted bars
corresponded to the following words: ”ﬁnancial information”, ”services”, ”free”, and ”personal com1 Chasen
isdistributedathttp://chasen.naist.jp/hiki/ChaSen/.
termex is distributed at http://gensen.dl.itc.u-tokyo.ac.jp/.

puter”, and actually many of articles in the clickedcluster were about on-line ﬁnancial services.
We also clicked the other cluster indicated by
the circle [C] in Figure 3(lower), and observedthat several bars were then highlighted in nonblue colors in the left part, as shown in Figure3(lower). The highlighted bars corresponded to
the following words: ”company”, ”business”, ”investigation”, and ”personal computer”, and actually many of articles in the clicked cluster wereabout business innovations.
As a result of above operations, we discoveredthat there were two meaningful clusters of articles
that were related to USA and Internet.
Figure 4 is an example of visualization of thenewspaper articles in 1999. Here we focused on
several tall icons in a cluster in the left part (indicated by a circle [A]), and clicked the two tall
icons of keywords ”design” and ”Toyota” (nameof Japanese automobile company), and then several icons in the right part were highlighted. Here,icons corresponding to articles about design werehighlighted in red, and icons corresponding to articles about Toyota were highlighted as green. Intensities of the highlighted icons denoted the importance of these keywords.
From this visualization result, we observed two
clusters (indicated by two circles [B] and [C]) that
there were yellow icons inside. We clicked thecluster indicated by the circle [B] in Figure 4(upper), and observed that only two bars were thenhighlighted in non-blue colors in the left part, asshown in Figure 4(upper). The highlighted bars
corresponded to ”design” and ”Toyota”, and otherkeywords of names of companies or items were
not highlighted. We found that Toyota’s design
brought many hot topics in 1999, rather than designs by other companies or designs of other items.
We clicked the other cluster indicated by thecircle [C] in Figure 4(lower), and observed that
more than two bars were then highlighted in nonblue colors in the left part, as shown in Figure
4(lower). The highlighted bars corresponded to
the following words: ”Asahi Beer”, ”Matsushita
Electric Industrial”, and ”development”, in addition to ”design” and ”Toyota”. Articles in the
clicked cluster were about the joint brand by thesecompanies. As a result of above operations, we
discovered a cluster of such unexpected articles.

5.3 Feedback and Discussion
We asked 10 examinees to play with the presented technique and give us comments. All theexaminees were female university student studied
computer graphics, but none of them were expertsof document visualization. This section introduces

Extraction of articles
Calculation of(based on the annotation“business information”) keyword importance

Newspaper
corpusImportanceof keywords

Keyword-article
matrixChasentermex

Selection of
150 keyword

Figure 2. Processing flow for keyword-document matrix construction from Japanese newspaper corpus.

the feedback of examinees and discuss about the
future improvements of the technique.
Most of the examinees said that they could immediately understand the meaning of visualization
results and master the operation. Some of themsaid that height-based representation of keyword
importance, in the left side, was useful to focus
on hot keywords. Some of them also said thatcolor representation of document importance, in
the right side, was intuitive since hue denoted thecorresponding keyword and intensity denoted theimportance. Some of them said that it was very
interesting to represent importance of keywords ina document cluster when they clicked in the rightside, because they could imagine the contents ofthe cluster of documents from the highlighted keywords.
On the other hand, some of examinees mentioned problems of the presented technique. An
examinee claimed that category-based keyword
structuring may be better than current cooccurrence-based keyword clustering. Another examinee claimed that it might be sometimes confusing, because the left and right sides looked
somewhat similar, but semantics of operation werequite diﬀerent. Some others suggested interesting
future works, which are listed in the next section.

6

Conclusion

This paper presented a technique for visualizing
corpus data, which applies our own dual hierarchical data visualization technique. We extracted
thousands of articles from a Japanese newspaper
corpus, and constructed keyword-article matrices
based on the importance values of the keywords
in the articles. The paper presented several visualization results of the matrix data, and discussed
visual discoveries of trends from the articles.
A journal paper on this work [14] describes
more detail of algorithms and experiments.

Reﬂecting feedback of our examinees, we pointout the following as potential future works, including:• visualization of correlation of more than two
keywords,• application of more sophisticated clustering
algorithm such as co-clustering,• more usability tests and objective/subjective
evaluations,• visualization of non-newspaper corpus such as
database of technical papers or patents, and• application of the dual hierarchical data visualization to non-corpus data.

Acknowledgements
We appreciate Prof. Ichiro Kobayashi, Ochanomizu University, and Prof. Tsuneaki Kato, The
University of Tokyo, for their suggestions on document processing. Japanese newspaper corpus
has been provided by A Workshop on Multimodal
Summarization for Trend Information (MuST).
This work has been partially supported by Japan
Society of the Promotion of Science under Grantin-Aid ﬁr Scientiﬁc Research (C) No. 18500074.

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B

A

A
C

Figure 3. Example (1). The circle [A] surrounds icons corresponding to two keywords "USA"and "Internet", and the other circles [B] and [C] surround two clusters of different contentsof articles related to both two keywords.

A

B

C

A

Figure 4. Example (2). The circle [A] surrounds icons corresponding to two keywords"design" and "Toyota", and the other circles [B] and [C] surround two clusters of different
contents of articles related to both two keywords.