An efficient technique for protein sequence clustering and classification

Abstract

A technique to reduce time and space during protein sequence clustering and classification is presented. During training and testing phase, the similarity score value between a pair of sequences is determined by selecting a portion of the sequence instead of the entire sequence. It is like selecting a subset of features for sequence data sets. The experimental results of the proposed method show that the classification accuracy (CA) using the prototypes generated/used does not degrade much but the training and testing time are reduced significantly. Thus the experimental results indicate that the similarity score need not be calculated by considering the entire length of the sequence for achieving a good CA. Even space requirement is reduced during execution phase. We have tested this using K-medians, supervised K-medians and nearest neighbour classifier (NNC) techniques.