Enhanced soft subspace clustering integrating within-cluster and between-cluster information

Zhaohong Deng, Kup Sze Choi, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

176 Citations (Scopus)

Abstract

While within-cluster information is commonly utilized in most soft subspace clustering approaches in order to develop the algorithms, other important information such as between-cluster information is seldom considered for soft subspace clustering. In this study, a novel clustering technique called enhanced soft subspace clustering (ESSC) is proposed by employing both within-cluster and between-class information. First, a new optimization objective function is developed by integrating the within-class compactness and the between-cluster separation in the subspace. Based on this objective function, the corresponding update rules for clustering are then derived, followed by the development of the novel ESSC algorithm. The properties of this algorithm are investigated and the performance is evaluated experimentally using real and synthetic datasets, including synthetic high dimensional datasets, UCI benchmarking datasets, high dimensional cancer gene expression datasets and texture image datasets. The experimental studies demonstrate that the accuracy of the proposed ESSC algorithm outperforms most existing state-of-the-art soft subspace clustering algorithms.
Original languageEnglish
Pages (from-to)767-781
Number of pages15
JournalPattern Recognition
Volume43
Issue number3
DOIs
Publication statusPublished - 1 Mar 2010

Keywords

  • ε-insensitive distance
  • Gene expression clustering analysis
  • Soft subspace
  • Subspace clustering
  • Texture image segmentation
  • Weighted clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this