Abstract
This paper presents a hybrid relational clustering algorithm, termed as rough-fuzzy c-medoids, to cluster biological sequences. It comprises a judicious integration of the principles of rough sets, fuzzy sets, c-medoids algorithm, and amino acid mutation matrix used in biology. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-medoids, enables efficient selection of cluster prototypes. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated on different protein data sets.
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© 2008 Springer-Verlag Berlin Heidelberg
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Maji, P., Pal, S.K. (2008). Rough-Fuzzy Relational Clustering Algorithm for Biological Sequence Mining. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_42
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DOI: https://doi.org/10.1007/978-3-540-79721-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
Online ISBN: 978-3-540-79721-0
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