R. Das, D.K. Bhattacharyya, and J.K. Kalita
Gene expression, dissimilarity measure, clustering, density based,frequent itemset mining, nearest neighbour
This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the methods use a novel dissimilarity measure discussed in the paper. The clustering methods were experimented in light of real- life datasets and the methods have been established to perform satisfactorily. The methods were also compared with some well- known clustering algorithms and found to perform well in terms of homogeneity, silhouette and the z-score cluster validity measure.
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