Subspace clustering over high-dimensional data stream based on grid density and attribute relativity

被引：0

作者：

Huang, Guoyan ^{[1
,2
]}

Miao, Liyun ^{[1
,2
]}

Ren, Jiadong ^{[1
,2
]}

机构：

[1] College of Information Science and Engineering, Yanshan University, Qinhuangdao City

[2] The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

来源：

Advances in Information Sciences and Service Sciences | 2012年 / 4卷 / 17期

关键词：

Attribute relativity; Best interesting subspace; Clustering; Grid density;

D O I：

10.4156/AISS.vol4.issue17.10

中图分类号：

学科分类号：

摘要：

The traditional clustering algorithms often fail to detect meaningful clusters in high-dimensional data space. To improve the above shortcoming, we propose GDRH-Stream, a clustering method based on the attribute relativity and grid density for high-dimensional data stream, which consists of an online component and an offline component. First, the algorithm filters out redundant attributes by computing the relative entropy. Then we define a weighted attribute relativity measure and estimate the relativity of the non-redundant attributes, and form the attribute triple. At last, the best interesting subspaces are searched by the attribute triple. On the online component, GDRH-Stream maps each data object into a grid and updates the characteristic vector of the grid. On the offline component, when a clustering request arrives, the best interesting subspaces will be generated by attribute relativity. Then the original grid structure is projected to the subspace and a new grid structure is formed. The clustering will be performed on the new grid structure by adopting an approach based on the density grid. Experimental results show that GDRH-Stream algorithm has better quality and scalability.

引用

页码：91 / 99

页数：8

共 11 条

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