2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
Yosuke Onoue , Kyoto University, Japan
Koji Koyamada , Kyoto University, Japan
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.
Visual analytics, Bipartite graph, Data mining, Redundancy, Uncertainty, Algorithm design and analysis
Y. Onoue and K. Koyamada, "Quasi-biclique edge concentration: A visual analytics method for biclustering," 2017 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Seoul, South Korea, 2017, pp. 215-219.