Large microarray data sets have recently become common. However, most available clustering methods do not easily handle large microarray data sets due to their very large computational complexity and memory requirements. Furthermore, typical clustering methods construct oversimplified clusters that ignore subtle but meaningful changes in the expression patterns present in large microarray data sets. It is necessary to develop an efficient clustering method that identifies both absolute expression differences and expression profile patterns in different expression levels for large microarray data sets. This study presents CLIC, which meets the requirements of clustering analysis particularly but not limited to large microarray data sets. CLIC is based on a novel concept in which genes are clustered in individual dimensions first and in which the ordinal labels of clusters in each dimension are then used for further full dimension-wide clustering. CLIC enables iterative sub-clustering into more homogeneous groups and the identification of common expression patterns among the genes separated in different groups due to the large difference in the expression levels. In addition, the computation of clustering is parallelized, the number of clusters is automatically detected, and the functional enrichment for each cluster and pattern is provided. CLIC is freely available at http://gexp2.kaist.ac.kr/clic
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