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An Effective Technique for Clustering Incremental Gene Expression data

This paper presents a clustering technique (GenClus) for gene expression data which can also handle incremental data. It is designed based on density based approach. It retains the regulation information which is also the main advantage of the clustering. It uses no proximity measures and is therefore free of the restrictions offered by them. GenClus is capable of handling datasets which are updated incrementally. Experimental results show the efficiency of GenClus in detecting quality clusters over gene expression data. Our approach improves the cluster quality by identifying sub-clusters within big clusters. It was compared with some well-known clustering algorithms and found to perform well in terms of the z-score cluster validity measure.