Towards improving fuzzy clustering using support vector machine: Application to gene expression data

Pattern Recognition

Volumn 42, Issue 11, 2009, Pages 2744-2763

  Mukhopadhyay, Anirban ^a   Maulik, Ujjwal ^b  

^a UNIVERSITY OF KALYANI   (India)

^b JADAVPUR UNIVERSITY   (India)

Author keywords

Cluster validity indices; Fuzzy clustering; Gene ontology; Microarray gene expression data; Support vector machines; Variable string length genetic algorithm

Indexed keywords

BIOLOGICAL SIGNIFICANCE; CLUSTER VALIDITY INDICES; CLUSTERING SCHEME; CLUSTERING SOLUTIONS; CLUSTERING TECHNIQUES; COMPUTATIONAL ANALYSIS; DATA-MINING TOOLS; EXPRESSION LEVELS; FUZZY C MEANS CLUSTERING; GENE EXPRESSION DATA; GENE ONTOLOGY; MICROARRAY DATA; MICROARRAY GENE EXPRESSION DATA; MICROARRAY TECHNOLOGIES; MULTIPLE COMPARISON TEST; POSTERIORI; REAL-CODED; STATISTICAL SIGNIFICANCE; STATISTICAL SIGNIFICANCE TEST; TIME POINTS; VARIABLE STRING LENGTH; VARIABLE STRING LENGTH GENETIC ALGORITHM;

ANALYSIS OF VARIANCE (ANOVA); BIOACTIVITY; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA MINING; FUZZY SYSTEMS; GEARS; GENE EXPRESSION; GENETIC ALGORITHMS; IMAGE RETRIEVAL; INFORMATION MANAGEMENT; MINING; MULTILAYER NEURAL NETWORKS; ONTOLOGY; REGRESSION ANALYSIS; SET THEORY; SUPPORT VECTOR MACHINES; VECTORS;

FUZZY CLUSTERING;

EID: 67649403094     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.04.018     Document Type: Article

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