Sardar, Tanvir H.
Mapreduce-based fuzzy C-means algorithm for distributed document clustering - Vol.103(1), Feb - New York Springer 2022 - 131-142p.
The clustering of big data is a challenging task. The traditional clustering algorithms are inefficient for clustering big data. The recent researches in this field suggest that the traditional clustering algorithms needed to be redesigned for the modern architecture of computing. This wok has proposed a novel MapReduce-based fuzzy C-means algorithm for big document data clustering. The algorithm is extensively experimented with using different sizes of document datasets and executed over the Hadoop cluster of different sizes. The proposed algorithm’s efficiency is compared against serial traditional fuzzy C-means and MapReduce-based K-means algorithms. The proposed design of the fuzzy C-means algorithm is scaled well with the Hadoop platform and documents big datasets and resulted in a performance gain.
Humanities and Applied Sciences
Mapreduce-based fuzzy C-means algorithm for distributed document clustering - Vol.103(1), Feb - New York Springer 2022 - 131-142p.
The clustering of big data is a challenging task. The traditional clustering algorithms are inefficient for clustering big data. The recent researches in this field suggest that the traditional clustering algorithms needed to be redesigned for the modern architecture of computing. This wok has proposed a novel MapReduce-based fuzzy C-means algorithm for big document data clustering. The algorithm is extensively experimented with using different sizes of document datasets and executed over the Hadoop cluster of different sizes. The proposed algorithm’s efficiency is compared against serial traditional fuzzy C-means and MapReduce-based K-means algorithms. The proposed design of the fuzzy C-means algorithm is scaled well with the Hadoop platform and documents big datasets and resulted in a performance gain.
Humanities and Applied Sciences