[1] Lu Yansheng, Hu Rong, Zou Lei, Zhou Chong, et al. Mining maximal pattern-based subspace clustersin high dimensional space [J]. Journal of Southeast University (English Edition), 2006, 22 (4): 490-495. [doi:10.3969/j.issn.1003-7985.2006.04.010]

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Mining maximal pattern-based subspace clustersin high dimensional space()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 4

Page:

490-495

Research Field:

Computer Science and Engineering

Publishing date:

2006-12-30

Info

Title:

Mining maximal pattern-based subspace clustersin high dimensional space

Author(s):

Lu Yansheng;  Hu Rong;  Zou Lei;  Zhou Chong

School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Keywords:

subspace clustering;  pattern similarity;  maximal pattern-based subspace clusters

PACS:

TP311

DOI:

10.3969/j.issn.1003-7985.2006.04.010

Abstract:

The problem of pattern-based subspace clustering, a special type of subspace clustering that uses pattern similarity as a measure of similarity, is studied.Unlike most traditional clustering algorithms that group the close values of objects in all the dimensions or a set of dimensions, clustering by pattern similarity shows an interesting pattern, where objects exhibit a coherent pattern of rise and fall in subspaces.A novel approach, named EMaPle to mine the maximal pattern-based subspace clusters, is designed.The EMaPle searches clusters only in the attribute enumeration spaces which are relatively few compared to the large number of row combinations in the typical datasets, and it exploits novel pruning techniques.EMaPle can find the clusters satisfying coherent constraints, size constraints and sign constraints neglected in MaPle.Both synthetic data sets and real data sets are used to evaluate EMaPle and demonstrate that it is more effective and scalable than MaPle.

References:

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Memo

Memo:

Biography: Lu Yansheng(1949—), male, professor, lys@mail.hust.edu.cn.

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