CABOSFV algorithm for high dimensional sparse data clustering

被引：6

作者：

Wu, S ^{[1
]}

Gao, XD ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Management, Beijing 100083, Peoples R China

来源：

JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING | 2004年 / 11卷 / 03期

关键词：

clustering; data mining; sparse; high dimensionality;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV), was proposed for the high dimensional clustering of binary sparse data. This algorithm compresses the data effectively by using a tool 'Sparse Feature Vector', thus reduces the data scale enormously, and can get the clustering result with only one data scan. Both theoretical analysis and empirical tests showed that CABOSFV is of low computational complexity. The algorithm finds clusters in high dimensional large datasets efficiently and handles noise effectively.

引用

页码：283 / 288

页数：6

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