Combining multiple clusterings via k-modes algorithm

Authors:
Huilan Luo;Fansheng Kong;Yixiao Li
Affiliations:
Artificial Intelligence Institute, Zhejiang University, Hangzhou, China;Artificial Intelligence Institute, Zhejiang University, Hangzhou, China;Artificial Intelligence Institute, Zhejiang University, Hangzhou, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the k-modes algorithm is proposed in this paper. A combined partition is found as a solution to the corresponding categorical data clustering problem using the k-modes algorithm. This study compares the performance of the k-modes consensus algorithm with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method.