A visual approach to improve clustering based on cluster ensembles

Jianping Zhou; Shawn Konecni; Kenneth Marx; Georges Grinstein

doi:10.1117/12.844249

18 January 2010 A visual approach to improve clustering based on cluster ensembles

Jianping Zhou, Shawn Konecni, Kenneth Marx, Georges Grinstein

Proceedings Volume 7530, Visualization and Data Analysis 2010; 753009 (2010) https://doi.org/10.1117/12.844249
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

Iterative clustering (e.g. K-Means, EM) is one of the most commonly used clustering methods, which attempts to iteratively find a local optimum starting from an initial condition, including initial centroids and initial number of clusters. For iterative clustering, research has shown that the initial conditions are crucial to clustering quality and running time of a clustering computation. Using a novel visualization tool, CComViz (Cluster Comparison Visualization), we present an innovative approach to refine the initial centroids and the number of clusters by visually analyzing multiple clustering results generated by different clustering algorithms. As an example, we apply our new approach to a gene expression case study for generating a better and converging clustering. The proposed approach is considered to be an extension to cluster ensembles since the original data sources are reused, while in classic cluster ensembles they are not.

Citation Download Citation

Jianping Zhou, Shawn Konecni, Kenneth Marx, and Georges Grinstein "A visual approach to improve clustering based on cluster ensembles", Proc. SPIE 7530, Visualization and Data Analysis 2010, 753009 (18 January 2010); https://doi.org/10.1117/12.844249

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