Clustering mixed data based on evidence accumulation

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

An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity matrix to extract a partition of the data. The performance of EBSC has been studied on real data sets. Results demonstrate the effectiveness of this algorithm in clustering mixed data tasks. Comparisons with other related clustering schemes illustrate the superior performance of this approach.