Title :
Cluster validation for subspace clustering on high dimensional data
Author :
Chen, Lifei ; Jiang, Qingshan ; Wang, Shengrui
Author_Institution :
Sch. of Math. & Comput. Sci., Fujian Normal Univ., Fuzhou
fDate :
Nov. 30 2008-Dec. 3 2008
Abstract :
As an important issue in cluster analysis, cluster validation is the process of evaluating performance of clustering algorithms under varying input conditions. Many existing methods address clustering results of low-dimensional data. This paper presents new solution to the problem of cluster validation for subspace clustering on high dimensional data. We first propose two new measurements for the intra-cluster compactness and inter-cluster separation of subspace clusters. Based on these measurements and the conventional indices, three new cluster validity indices that can be applied to subspace clustering are presented. Combining with a soft subspace clustering algorithm, the new indices are used to determine the number of clusters in high dimensional data. The experimental results on synthetic and real world datasets have shown their effectiveness.
Keywords :
pattern clustering; cluster analysis; cluster validation; high dimensional data; inter-cluster separation; intra-cluster compactness measurement; subspace clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computer science; Data analysis; Machine learning; Mathematics; Performance analysis; Software algorithms; Software performance; Statistical analysis;
Conference_Titel :
Circuits and Systems, 2008. APCCAS 2008. IEEE Asia Pacific Conference on
Conference_Location :
Macao
Print_ISBN :
978-1-4244-2341-5
Electronic_ISBN :
978-1-4244-2342-2
DOI :
10.1109/APCCAS.2008.4746001