Gene Expression Data Clustering using a Fuzzy Link based Approach
نویسنده
چکیده
There are many clustering algorithms for gene expression data in the literature that are robust against noise and outliers. The limitation with many of these algorithms is that they cannot identify the overlapping and intersecting clusters. This paper presents an algorithm for clustering gene expression data using the concepts of common neighbors and fuzzy clustering for detecting intersecting and overlapping clusters. On comparison of the algorithm to the existing popular approaches it was found that our algorithm gives good results in terms of z-score measure of cluster validity and p-value and Q-value measures.
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