Rough-fuzzy relational clustering algorithm for biological sequence mining

Authors:
Pradipta Maji;Sankar K. Pal
Affiliations:
Center for Soft Computing Research, Machine Intelligence Unit, Indian Statistical Institute, India;Center for Soft Computing Research, Machine Intelligence Unit, Indian Statistical Institute, India
Venue:
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Year:
2008

Citing 3
Cited 0

Artificial neural network model for predicting HIV protease cleavage sites in protein

Advances in Engineering Software
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Low-complexity fuzzy relational clustering algorithms for Web mining

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

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.