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Proceedings Paper

A visual approach to improve clustering based on cluster ensembles
Author(s): Jianping Zhou; Shawn Konecni; Kenneth Marx; Georges Grinstein
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Paper 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.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7530, Visualization and Data Analysis 2010, 753009 (18 January 2010); doi: 10.1117/12.844249
Show Author Affiliations
Jianping Zhou, Univ. of Massachusetts Lowell (United States)
Shawn Konecni, Univ. of Massachusetts Lowell (United States)
Kenneth Marx, Univ. of Massachusetts Lowell (United States)
Georges Grinstein, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 7530:
Visualization and Data Analysis 2010
Jinah Park; Ming C. Hao; Pak Chung Wong; Chaomei Chen, Editor(s)

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