Clustering analysis of gene expression data based on semi-supervised Visual Clustering Algorithm

Fu Lai Korris Chung, Shitong Wang, Zhaohong Deng, Chen Shu, D. Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


When gene expression datasets contain some labeled data samples, the labeled information should be incorporated into clustering algorithm such that more reasonable clustering results can be achieved. In this paper, a novel semi-supervised clustering algorithm, Semi-supervised Iterative Visual Clustering Algorithm (Semi-IVCA), is presented to tackle with such datasets. The new algorithm first constructs the visual sampling image of the dataset based on visual theorem and obtains its attractors using the gradient learning rules, where each attractor denotes a cluster of the dataset. Then the new algorithm introduces an iterative clustering procedure to realize the semi-supervised learning. The new algorithm is a generalization of the current Visual Clustering Algorithm (VCA) presented by authors. Except for the advantage that Semi-IVCA can effectively utilize the labeled data information in clustering, it is robust and insensitive to initialization, and it has strong parameter learning capability and good interpretation for the clustering results. When the new algorithm Semi-IVCA is applied to the artificial and real gene expression datasets, the experimental results confirm the above advantages of algorithm Semi-IVCA.
Original languageEnglish
Pages (from-to)981-993
Number of pages13
JournalSoft Computing
Issue number11
Publication statusPublished - 1 Sept 2006


  • Clustering analysis
  • Gene expression data
  • Gradient system
  • Semi-supervised learning
  • Visual clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


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