Knowledgeable Clustering of Microarray Data
作者： George Potamias
DOI: 10.1007/978-3-540-30547-7_49
关键词: Pattern recognition 、 Graph (abstract data type) 、 Clustering high-dimensional data 、 Hierarchical clustering 、 Artificial intelligence 、 CURE data clustering algorithm 、 Data mining 、 Correlation clustering 、 Cluster analysis 、 Minimum spanning tree 、 Single-linkage clustering 、 Computer science
摘要: A novel graph-theoretic clustering (GTC) is presented. The method relies on a weighted graph arrangement of the genes, and iterative partitioning respective minimum spanning tree graph. final result hierarchical genes. GTC utilizes information about functional classification genes to knowledgeably guide process achieve more informative results. was applied tested an indicative real-world domain producing satisfactory biologically valid Future R&D directions are also posted.
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