DBSC: A dependency-based subspace clustering algorithm for high dimensional numerical datasets

被引：0

作者：

Wang, Xufei ^{[1
]}

Li, Chunping ^{[1
]}

机构：

[1] Tsinghua Univ, Sch Software, China MOE Key Lab Informat Syst Secur, Beijing, Peoples R China

来源：

AI 2007: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2007年 / 4830卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a novel algorithm called DBSC, which finds subspace clusters in numerical datasets based on the concept of "dependency". This algorithm uses a depth-first search strategy to find out the maximal subspaces: a new dimension is added to current k-subspace and its validity as a (k+1)-subspace is evaluated. The clusters within those maximal subspaces are mined in a similar fashion as maximal subspace mining does. With the experiments on synthetic and real datasets, our algorithm is shown to be both effective and efficient for high dimensional datasets.

引用

页码：832 / 837

页数：6

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