Quasi-biclique edge concentration: A visual analytics method for biclustering

Biclustering is a well-known approach for data mining, and it is applied in many fields, such as genome analyses, security services, and social network analyses. Biclustering finds bicliques contained in a bipartite graph. However, in real data, a biclique may lack several edges because of various r...

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Veröffentlicht in:IEEE Pacific Visualization Symposium S. 215 - 219
Hauptverfasser: Onoue, Yosuke, Koyamada, Koji
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2017
Schlagworte:
Algorithm design and analysis
Bipartite graph
Data mining
G.2.2 [Discrete Mathematics]: Graph Theory- Graph Algorithms
H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-Clustering
Redundancy
Uncertainty
Visual analytics
ISSN:2165-8773
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Zusammenfassung:Biclustering is a well-known approach for data mining, and it is applied in many fields, such as genome analyses, security services, and social network analyses. Biclustering finds bicliques contained in a bipartite graph. However, in real data, a biclique may lack several edges because of various reasons, such as errors. In this situation, traditional biclustering methods cannot find correct biclusters. A novel biclustering method that can analyze real data under uncertainty is needed. Quasi-biclique is a mathematical concept that represents incomplete bicliques. We propose the quasi-biclique edge concentration (QBEC) method, which is a visual analysis method for biclustering using quasi-biclique mining. QBEC includes visual representations and user interactions for quasi-bicliques. Quasi-bicliques contained in a bipartite graph are represented based on edge concentration. The incompleteness of a quasi-biclique is reflected in edge opacity. Users can interactively explore data by adjusting the incompleteness parameter of the quasi-biclique. We demonstrate the effectiveness of QBEC using real-world data.
ISSN:2165-8773
DOI:10.1109/PACIFICVIS.2017.8031597