iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

Clustering plays an important role in many large‐scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms of...

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Veröffentlicht in:Computer graphics forum Jg. 31; H. 3pt3; S. 1155 - 1164
Hauptverfasser: Lee, Hanseung, Kihm, Jaeyeon, Choo, Jaegul, Stasko, John, Park, Haesun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.06.2012
Schlagworte:
Clustering
Clusters
Computer graphics
Data analysis
Data mining
Database Management [H.2.8]: Database Applications-Data mining
Dirichlet problem
Information Systems [H.1.2]: User/Machine Systems-Human information processing
Interactive
Mathematical models
Studies
Trees
Visual
Visualization
ISSN:0167-7055, 1467-8659
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Zusammenfassung:Clustering plays an important role in many large‐scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widely‐used topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph‐based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways. Keywords can be adjusted so that they characterize each cluster better. In addition, our system can filter out noisy data and re‐cluster the data accordingly. Cluster hierarchy can be constructed using a tree structure and for this purpose, the system supports cluster‐level interactions such as sub‐clustering, removing unimportant clusters, merging the clusters that have similar meanings, and moving certain clusters to any other node in the tree structure. Furthermore, the system provides document‐level interactions such as moving mis‐clustered documents to another cluster and removing useless documents. Finally, we present how interactive clustering is performed via iVisClustering by using real‐world document data sets.
Bibliographie:istex:AD6CD88F944DA2C69C4EE714EEA697CEC3240CF4
ark:/67375/WNG-18NVXGKW-S
ArticleID:CGF3108
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2012.03108.x