Tapered Assessment on Distributed Clustering vital in Protein Sequence Environment

Abstract

__ The ever-increasing size of data sets, poor scalability, space and time of execution of clustering algorithm has haggard attention to distributed clustering for partitioning large data sets. Protein sequence prediction is one of the vital roles in bioinformatics, which is used to analyze the biological data. The combination of Distributed clustering algorithm and soft computing techniques used to discover the gene/protein structure or sequence. Soft computing is a collection of algorithms that are employed for finding a solution because of their ability to handle imprecision, uncertainty in large and complex problem. The vital role of distributed clustering algorithm is to cluster the distributed datasets without collecting all the data into single site. Cluster the data locally and extract the representatives of these clusters and send to global site where the cluster based on local representative. It deals with large homogenous/heterogeneous data for any application using soft computing approaches. Clustering is the process of similar object grouped into one cluster and dissimilar object grouped in other. The effort is being taken to progress the efficiency of distributed combining algorithm using different soft computing techniques for protein data.