Subspace clustering of high dimensional data streams

被引：5

作者：

Wang, Shuyun ^{[2
]}

Fan, Yingjie ^{[2
]}

Zhang, Chenghong ^{[1
]}

Xu, HeXiang ^{[2
]}

Hao, Xiulan ^{[2
]}

Hu, Yunfa ^{[2
]}

机构：

[1] Fudan Univ, Sch Management, Shanghai 200433, Peoples R China

[2] Fudan Univ, Dept Comp & Informat Technol, Shanghai 200433, Peoples R China

来源：

7TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE/ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICIS.2008.58

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, SOStream, which is a novel algorithm of clustering over high dimensional online data stream is presented, it is based on subspace. SOStream partitions the data space into grids, and maintains a superset of all dense units in an online way. A deterministic lower and upper bound of the selectivity of each maintained units are also given. With the maintained potential dense units, SOStream is capable of discovering the clusters in different subspaces over high dimensional data stream with arbitrary shape. The experimental results on real and synthetic datasets demonstrate the effectivity of the approach.

引用

页码：165 / +

页数：2

共 13 条

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