Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15470
Title: Clustering analysis of gene expression data based on semi-supervised Visual Clustering Algorithm
Authors: Chung, FL 
Wang, S
Deng, Z
Shu, C
Hu, D
Keywords: Clustering analysis
Gene expression data
Gradient system
Semi-supervised learning
Visual clustering
Issue Date: 2006
Publisher: Springer
Source: Soft computing, 2006, v. 10, no. 11, p. 981-993 How to cite?
Journal: Soft Computing 
Abstract: 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.
URI: http://hdl.handle.net/10397/15470
ISSN: 1432-7643
DOI: 10.1007/s00500-005-0025-7
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