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- Title
APPLYING ROBUST DIRECTIONAL SIMILARITY BASED CLUSTERING APPROACH RDSC TO CLASSIFICATION OF GENE EXPRESSION DATA.
- Authors
LI, H. X; WANG, SHITONG; XIU, YU
- Abstract
Despite the fact that the classification of gene expression data from a cDNA microarrays has been extensively studied, nowadays a robust clustering method, which can estimate an appropriate number of clusters and be insensitive to its initialization has not yet been developed. In this work, a novel Robust Clustering approach, RDSC, based on the new Directional Similarity measure is presented. This new approach RDSC, which integrates the Directional Similarity based Clustering Algorithm, DSC, with the Agglomerative Hierarchical Clustering Algorithm, AHC, exhibits its robustness to initialization and its capability to determine the appropriate number of clusters reasonably. RDSC has been successfully employed to both artificial and benchmarking gene expression datasets. Our experimental results demonstrate its distinctive superiority over the conventional method Kmeans and the two typical directional clustering algorithms SPKmeans and moVMF.
- Subjects
GENE expression; CLUSTERING of particles; GAUSSIAN distribution; COMPUTATIONAL biology; BIOINFORMATICS; EUCLIDEAN algorithm
- Publication
Journal of Bioinformatics & Computational Biology, 2006, Vol 4, Issue 3, p745
- ISSN
0219-7200
- Publication type
Article
- DOI
10.1142/S0219720006002144