Feature weighted kernel clustering with application to medical data analysis

Hong Jia, Yiu Ming CHEUNG

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


Clustering technique is an effective tool for medical data analysis as it can work for disease prediction, diagnosis record mining, medical image segmentation, and so on. This paper studies the kernel-based clustering method which can conduct nonlinear partition on input patterns and addresses two challenging issues in unsupervised learning environment: feature relevance estimate and cluster number selection. Specifically, a kernel-based competitive learning paradigm is presented for nonlinear clustering analysis. To distinguish the relevance of different features, a weight variable is associated with each feature to quantify the feature's contribution to the whole cluster structure. Subsequently, the feature weights and cluster assignment are updated alternately during the learning process so that the relevance of features and cluster membership can be jointly optimized. Moreover, to solve the problem of cluster number selection, the cooperation mechanism is further introduced into the presented learning framework and a new kernel clustering algorithm which can automatically select the most appropriate cluster number is educed. The performance of proposed method is demonstrated by the experiments on different medical data sets.

Original languageEnglish
Title of host publicationBrain and Health Informatics - International Conference, BHI 2013, Proceedings
Number of pages10
Publication statusPublished - 2013
EventInternational Conference on Brain and Health Informatics, BHI 2013 - Maebashi, Japan
Duration: 29 Oct 201331 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8211 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Brain and Health Informatics, BHI 2013

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Competitive learning
  • Cooperation mechanism
  • Feature weight
  • Kernel-based clustering
  • Number of clusters


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