Paper Info
Title
Mining Representative Subspace Clusters in High-dimensional Data
Abstract
A major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high computational complexity, which severely restricts the usage of subspace clustering. The problem gets even worse with the increase of the data’s dimensionality. In this paper, we propose to mine the representative subspace clusters in high-dimensional data to alleviate the problem. Typically, subspace clusters can be clustered further into groups, and several representative clusters can be generated from each group. Unfortunately, when the size of the set of representative clusters is specified, the problem of finding the optimal set is NP-hard. To solve this problem efficiently, we present an approximate method PCoC. The greatest advantage of our method is that we only need a subset of subspace clusters as the input. Our performance study shows the effectiveness and efficiency of the method.
Year
DOI
Venue
2009
10.1109/FSKD.2009.463
FSKD (1)
Keywords
Field
DocType
high-dimensional data,approximate method,optimal set,subspace cluster,mining representative subspace cluster,representative cluster,greatest advantage,subspace clustering,explosive number,mining representative subspace clusters,high computational complexity,representative subspace cluster,clustering algorithms,polynomials,high dimensional data,data mining,np hard problem,silicon,computational complexity,chromium,strontium
Cluster (physics),Clustering high-dimensional data,Polynomial,Subspace topology,Pattern recognition,Random subspace method,Computer science,Curse of dimensionality,Artificial intelligence,Cluster analysis,Machine learning,Computational complexity theory
Conference
Citations 
PageRank 
References 
2
0.41
15
Authors
5
Name
Order
Citations
PageRank
Guanhua Chen1223.38
Xiuli Ma29215.47
YANG Dong-Qing3975201.51
Shiwei Tang447851.52
Meng Shuai5404.76