Fuzzy forms of the rand , adjusted rand and jaccard indices for fuzzy partitions of gene expression and other data
2017-11-21T00:23:49Z (GMT) by
Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. Clustering algorithms have been proposed for the analysis of gene expression data with little guidance available to help choose among them however. Since the output of clustering is a partition of the input data, the quality of the partition must be determined. This paper presents fuzzy extensions to some commonly used clustering measures including the rand index (RI), adjusted rand index(ARI) and the jaccard index(JI) that are already defined for crisp clustering. Fuzzy clustering, and therefore fuzzy cluster indices, is beneficial since it provides more realistic cluster memberships for the objects that are clustered rather than 0 or 1 values. If a crisp partition is still desired the fuzzy partition can be turned in to a crisp partition in an obvious manner. The usefulness of the fuzzy clustering in that case is that it processes noise better. These new indices proposed in this paper, called FRI, FARI, and FJI for fuzzy clustering, give the same values as the original indices do in the special case of crisp clustering. Through use in fuzzy clustering of artificial data and real data, including gene expression data, the effectiveness of the indices is demonstrated. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.