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Author Popescu, M. ♦ Bezdek, J.C. ♦ Keller, J.M. ♦ Havens, T.C. ♦ Huband, J.M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Conferences ♦ Correlation ♦ Fuzzy systems ♦ Partitioning algorithms ♦ Biology ♦ Gene expression ♦ Clustering algorithms
Abstract Many important applications in biology have underlying datasets that are relational, that is, only the (dis)similarity between biological objects (amino acid sequences, gene expression profiles, etc.) is known and not their feature values in some feature space. Examples of such relational datasets are the gene similarity matrices obtained from BLAST, gene expression data, or gene ontology (GO) similarity measures. Once a relational dataset is obtained, a common question asked is how many groups of objects are represented in the original dataset. The answer to this question is usually obtained by employing a clustering algorithm and a cluster validity measure. In this article we describe a cluster validity measure for non-Euclidean relational fuzzy c-means that is based on the correlation between a relation induced on the data by the cluster memberships and the original relational data. This validity measure can be applied to partitions made by any fuzzy relational clustering algorithm. We illustrate our measure by validating clusters in several dissimilarity matrices for a set of 194 gene products obtained using BLAST and GO similarities.
Description Author affiliation: Heath Manage. & Med. Inf. Dept., Univ. of Missouri, Columbia, MO (Popescu, M.)
ISBN 9781424418183
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-01
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 278.08 kB
Page Count 6
Starting Page 726
Ending Page 731


Source: IEEE Xplore Digital Library