• Kaveri M More
• Prof. R. P. Dahake

Co-clustering is powerful tool developed for 2D co-occurrences as well as bipartite data. There is need of significant computational resources for huge data processing. Traditional approaches of co-clustering suffered from issues of graph partitioning. From study of existing system expectation-maximization (EM) algorithms such as, k-means clustering algorithms gives the provable evidence with the sequential updates. Without affecting result, it reduces computational cost. In this proposed approach, sequential updates in a distributed environment is proposed using parallelize Alternate Minimization Co-Clustering (AMCC) algorithm. The AMCC algorithm is again extended using fast nonnegative matrix tri-factorization (FNMTF). The proposed work will also contribute attribute based filtering of input dataset using knn attribute filtration at the time of row and column shuffling process. Performance of the proposed system is evaluated on local machine and network of machines. Proposed AMCC algorithm with feature selection technique achieves better efficiency than existing algorithm

Co-Clustering, concurrent updates, sequential updates, distributed framework.

"Clustering of Documents with Sequential Update in Distributed Environment ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.568-573, September-2018, Available :http://www.jetir.org/papers/JETIR1809589.pdf

"Clustering of Documents with Sequential Update in Distributed Environment ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp568-573, September-2018, Available at : http://www.jetir.org/papers/JETIR1809589.pdf