Co-Clustered: Data Co-Clustering using Distributed Framework for Sequential Restores

Co-clustering is an effective data mining tool for existence and diploid data. As data sets become increasingly large, the scalability of existence becomes increasingly crucial. In this paper, we propose two approaches to parallelize co-clustering with subsequent amend in a distributed environment. Based on these two approaches, we present a new distributed framework, Co-ClusterD, that backing effective utilization of co-clustering algorithms with sequential updates. We developed and applied Co-ClusterD, and give its ability through two co-clustering algorithms: information theoretic co-clustering (ITCC), fast nonnegative matrix tri-factorization (FNMTF). We evaluate our framework on both a local cluster of the systems and the Amazon EC2 cloud. Our assessment gives that co-clustering algorithms implemented in Co-ClusterD can attain excellent outputs and run faster than their traditional concurrent counterparts.