An Effective High Attribute Dimensional Sparse Clustering
ZHAO YaQin1, ZHOU XianZhong2, HE Xin1, WANG JianYu1
1.School of Automation, Nanjing University of Science and Technology, Nanjing 210094 2.School of Management and Engineering, Nanjing University, Nanjing 210093
Abstract Clustering analysis is one of the most important techniques in data mining with scale, dimension and sparseness of dataset being three key factors that influence accuracy of clustering. An effective clustering algorithm for the high attribute dimension sparse data is proposed in this paper. Definitions are given, such as sparse similarity, similarity between equivalence relations and generalized equivalence relation. Based on these definitions, the theory of equivalence relation is applied to form initial clusters. Initial equivalence relations are modified in terms of the similarity between two equivalence relations in order to obtain more reasonable clustering results. High dimensional sparse data is effectively compressed and expressed as sparse feature vector whose dimension is far lower than that of original data. As a result, the proposed approach can handle an array of high dimensional sparse data with high efficiency, and be independent of sequence of the objects.
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2006, Vol. 19 
