一种有效的高属性维稀疏数据聚类算法<sup>*</sup>

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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.

Key words： Sparse Similarity Similarity between Equivalence Relations Data Compression Clustering

Received: 07 January 2005

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	Articles by authors
	ZHAO YaQin
	ZHOU XianZhong
	HE Xin
	WANG JianYu

Cite this article:

ZHAO YaQin,ZHOU XianZhong,HE Xin等. An Effective High Attribute Dimensional Sparse Clustering[J]. , 2006, 19(3): 289-294.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I3/289

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