Efficient median based clustering and classification techniques for protein sequences

Abstract In this paper, an efficient K-medians clustering (unsupervised) algorithm for prototype selection and Supervised K-medians (SKM) classification technique for protein sequences are presented. For sequence data sets, a median string/sequence can be used as the cluster/group representative. In K-medians clustering technique, a desired number of clusters, K, each represented by a median string/sequence, is generated and these median sequences are used as prototypes for classifying the new/test sequence whereas in SKM classification technique, median sequence in each group/class of labelled protein sequences is determined and the set of median sequences is used as prototypes for classification purpose. It is found that the K-medians clustering technique outperforms the leader based technique and also SKM classification technique performs better than that of motifs based approach for the data sets used. We further use a simple technique to reduce time and space requirements during p...

• Type: Journal
• Year: 2006
• Where: PAA
• Authors: P. A. Vijaya, M. Narasimha Murty, D. K. Subramanian
• Keywords: Classification Technique, Distributed And Parallel Computing, Median Sequences, PAA 2006, Protein Sequence