An optimization algorithm for clustering using weighted dissimilarity measures

doi:10.1016/j.patcog.2003.11.003

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An optimization algorithm for clustering using weighted dissimilarity measures

2004-05-01

207

PRE

Wai Ki Ching

Michael K. Ng

Joshua Zhexue Huang

DOI：10.1016/j.patcog.2003.11.003

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Abstract

En 中文

One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

clustering

data mining

optimization

attributes weights

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