Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids

被引：11

作者：

Sardar T.H. ^{[1
]}

Ansari Z. ^{[2
]}

机构：

[1] School of Computer Science & Engineering, Jain University, Bangalore

[2] Electrical Engineering Section, University Polytechnic, Aligarh Muslim University, Aligarh

来源：

Journal of The Institution of Engineers (India): Series B | 2022年 / 103卷 / 01期

关键词：

Big data; Distributed computing; Distributed fuzzy C-Medoids; Document clustering; Hadoop; MapReduce;

D O I：

10.1007/s40031-021-00647-w

中图分类号：

学科分类号：

摘要：

Efficient big data clustering is a requirement for massive data generating in this digitalized connected world. The traditional clustering algorithms do not scale over massively sized and highly unstructured big data. Thus, to obtain efficiency in clustering big data new architecture and programming paradigm is required. In this work, a novel MapReduce-based Fuzzy C-Medoids clustering algorithm is designed and experimented with to cluster big data repository of documents datasets. The performance of the proposed algorithm is experimentally evaluated for different-sized Hadoop cluster sizes and different-sized document datasets. The algorithm is found to be scalable and efficient in performing clustering jobs. © 2021, The Institution of Engineers (India).

引用

页码：73 / 82

页数：9

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