A Distributed and Parallel Clustering Algorithm for Massive Biological Data

Distributed processing today is a largely advantageous technology of bridging together a system of multiple computers and processor systems in running applications. The concept of Distributed processing has allowed time cutting and therefore reduction in costs. Using this, we aim to address clustering techniques in developing new method for further reduction in time and costs. The problem of clustering huge amount of data is a very time consuming operation. So by applying parallel and distributed approach, we can minimize the total time necessary for clustering the data. In this research, a parallel and distributed version of k-means clustering algorithm is proposed. The proposed algorithm will be implemented using Matlab, and will be tested with large synthetic data sets.