Visualization of Corpus Databy a Dual Hierarchical Data Visualization TechniqueHaruho Tachibana∗Takayuki Itoh†

Graduate School of Humanitics andSciences, Ochanomizu University

Graduate School of Humanitics andSciences, Ochanomizu University

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

Documents

r1
r2…
rn

c1 c2

…

cm

a11 a12a21 a22…
an1 an2…
…

a1m
a2m…

anm

Vector representationof columns

Clusters of columns

Vector representationof rows
r1=(a11, …, a1m )…
rn=(an1, …, anm )c1=(a11, …, an1)…
cm =(a1m , …, anm )

c2,c3

c1,cn

Clusters of rows
r2,r3

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 our dual hierarchical data visualization technique. The technique first generates clusters of keywords and documents, and then visualizes the two sets of clusters applying dual
hierarchical data visualization technique.

A BSTRACTThis poster presents a technique for visualization of matrix data
applying dual hierarchical data visualization technique. The presented technique first applies hierarchical clustering for rows andcolumns of the matrix data. It then visualizes the two sets of clusters applying dual hierarchical data visualization components. The
poster also presents an application of the technique for visualization of keyword-document matrix generated from large-scale corpus data. The paper shows an example of visualization results ofcorpus data consists of thousands of Japanese newspaper articles,
and introduces an interesting trends discovered from the result.
1

M ATRIX DATA V ISUALIZATION BY A D UAL H IERARCHICALDATA V ISUALIZATION T ECHNIQUE
1.1 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 shownin Figure 2. Here the technique treats columns C = (c1 , .., cm ) asn-dimensional vectors, such as ci = (a1i , .., ani ). Similarly, the technique treats rows R = (r1 , .., rn ) as m-dimensional vectors, such asr j = (a j1 , .., a jm ). Value type of ai j can be binary, integer, real, orany others which clustering algorithms can be applied.
The technique generates clusters of columns by the following
procedure. It first calculates Euclidian distances between every∗ e-mail: haruho@itolab.is.ocha.ac.jp† e-mail: itot@computer.org

Figure 2: Clustering of columns and rows of matrix data.

possible pairs of columns. Let the distance between ci and c j as
di j , and the maximum distance of the all distances as Dmax . Herethe technique calculates the similarity value si j for di and d j , assi j = 1.0 − di j /Dmax , where 0 is the minimum value of si j , and 1
is the maximum value of si j . The technique then generates clusters
of columns, applying agglomerative clustering method. Our implementation simply generates combination of columns by iteratively
coupling the columns or groups of columns according to their similarity values, and then generates nested clusters by grouping thecolumns according user-defined threshold values.
Above processes are also applied to rows as well as columns.
Finally the technique generates two sets of nested clusters, and they
can be treated as two hierarchical data.
1.2 Visualization of Clusters
The technique then visualizes the two hierarchical data. Let the visualization module for rows as the left part, and the visualization
module for columns as the right part. Therefore, the left part visualizes n rows r1 to rn , and the right part visualizes m columns c1to cm . They represent columns and rows as three dimensional barcharts, where colors, shapes, and heights of the bars vary according
to application-oriented semantics.
1.3 Interaction between the Dual Hierarchical Data
Our technique provides two-way interaction between the left andright parts, so that users can interactively explore the data items.
When users click a row in the left part, then the technique highlights
columns in the right part, which are related to the clicked row in theleft part. Similarly, when users click a column in the right part, thenthe technique highlights rows in the left part, which are related tothe clicked column in the right part.
Suppose that a user clicks ri in the left part. The technique calculates visual attributes of c j using ai j , according to user-definedconditions. The conditions can be defined according to applicationoriented semantics. Consequently, the technique highlights interesting columns in the right part. Similarly, when a user clicks c j inthe right part, the technique calculates visual attributes of rows, andhighlights interesting rows in the left part.
2I MPLEMENTATION FOR C ORPUS DATA V ISUALIZATIONThis section presents an implementation of corpus data visualization applying our dual hierarchical data visualization technique. Letkeywords 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, whereai j is the importance of i-th keyword in the j-th document. It thendisplays the clustered keywords in the left part, and the clustereddocuments in the right part.
As shown in Figure 1, we modified visual attributes and interaction mechanism between the left and right parts to customize for
corpus data visualization as follows: 1) Heights of icons in the leftpart are proportional to the sum of importance, ∑mj=1 ci j for i-thkeyword. 2) Hues of icons in the left part represent the sum of importance in a specific cluster of documents, where redness denotesthat importance is high, and blueness denotes that importance islow. 3) A user can choice one of the conditions to filter the articles while calculating the sum of importance of the keywords. 4)
When a cursor points one of the icons of keywords, it indicates thekeyword. 5) When a user inputs a keyword, it highlights the icon,
which corresponds to the keyword, in the left part. 6) When a user
clicks an icon or a cluster in the left part, it indicates a list of keywords inside the cluster. 7) When a user clicks an icon of keyword
in the left part, the right part then represents the importance of thekeyword for each document by R of RGB values. 8) When a user
clicks another icon of keyword in the left part, the right part thenrepresents the importance of the keyword for each document by Gof RGB values. 9) When a user clicks an icon in the right part,it indicates the body of the document. 10) When a user clicks anicon or a cluster in the right part, the left part then calculates thehue of icons of keywords. The hue is calculated from the sum ofimportance of the keywords in the documents of the clicked cluster.
3E XPERIMENTS

3.1 Matrix Data Generation from a Japanese Newspaper
CorpusWe used a corpus of Japanese Mainichi newspaper in 1998 and1999, where articles are stored in XML format containing date,headline, body, and additional annotations. We extracted articleswhich 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 in 1998 and 1999 respectively, according to the sum of importance values. We applied”Chasen” 1 , an open software for morpheme analysis of Japanese
documents, and ”termex” 2 , also an open software for importancecalculation of words in Japanese documents. We looked over the200 words and manually selected 150 words in 1998 and 1999
respectively; here we preferentially selected name of companies,name of items, technical and financial terms, because we thoughtsuch words would bring trends of business information. Finally, wegenerated keyword-article matrices in 1998 and 1999 respectively,
and visualized them.
3.2 Example of Visualization Results
Figure 3 is an example of visualization of the newspaper articles in
1998. Here we clicked a cluster in the left part (indicated by a yellow circle), and looked a list of keywords in the cluster. We clicked
the two keywords ”USA” and ”Internet” in the list, and therefore1 Distributed at http://chasen.naist.jp/hiki/ChaSen/.
2 Distributed at http://gensen.dl.itc.u-tokyo.ac.jp/.

Figure 3: Example of visualization results.

several icons in the right part were highlighted. Here, red icons denote articles about Internet, green icons denote articles about USA,and their intensities denote the importance of these keywords.
From this visualization result, we observed two clusters (indicated by blue circles) that there were yellow icons inside. We
clicked the cluster indicated by the blue circle in Figure 3(upper),and observed that several bars are then highlighted as non-blue colors in the left part as shown in Figure 3(upper). The highlighted
bars corresponded to the following words: ”financial information”,”services”, ”free”, and ”personal computer”, and actually many ofarticles in the clicked cluster were about on-line financial services.
We also clicked the other cluster indicated by the blue circle
in Figure 3(lower), and observed that several bars are then highlighted as non-blue colors in the left part. The highlighted bars corresponded to the following words: ”company”, ”business”, ”investigation”, and ”personal computer”, and actually many of articles in
the clicked cluster were about business innovations of companies.
As a result of above operations, we discovered that there were
two meaningful clusters related to USA and Internet.
ACKNOWLEDGEMENTS
We appreciate Prof. Ichiro Kobayashi, Ochanomizu University, andProf. 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 Grant-in-Aid fir Scientific Research (C) No. 18500074.
R EFERENCES[1] Itoh T., Takakura H., Sawada A., Koyamada K., Hierarchical Visualization of Network Intrusion Detection Data in the IP Address Space,IEEE Computer Graphics and Applications, 26(2), 40-47, 2006.