Enhancing grid-density based clustering for high dimensional data

被引：21

作者：

Zhao, Yanchang

Cao, Jie ^{[1
]}

Zhang, Chengqi ^{[2
]}

Zhang, Shichao ^{[3
]}

机构：

[1] Nanjing Univ Finance & Econ, Jiangsu Prov Key Lab E Business, Nanjing 210003, Peoples R China

[2] Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia

[3] Guangxi Normal Univ, Coll CS & IT, Guilin, Australia

来源：

JOURNAL OF SYSTEMS AND SOFTWARE | 2011年 / 84卷 / 09期

关键词：

Clustering; Subspace clustering; High dimensional data; ALGORITHM;

D O I：

10.1016/j.jss.2011.02.047

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We propose an enhanced grid-density based approach for clustering high dimensional data. Our technique takes objects (or points) as atomic units in which the size requirement to cells is waived without losing clustering accuracy. For efficiency, a new partitioning is developed to make the number of cells smoothly adjustable; a concept of the ith-order neighbors is defined for avoiding considering the exponential number of neighboring cells; and a novel density compensation is proposed for improving the clustering accuracy and quality. We experimentally evaluate our approach and demonstrate that our algorithm significantly improves the clustering accuracy and quality. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：1524 / 1539

页数：16

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