PDC-Transitive: An Enhanced Heuristic for Document Clustering Based on Relational Analysis Approach and Iterative MapReduce

Yasmine Lamari () and Said Chah Slaoui ()
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Yasmine Lamari: Department of Computer Science, Faculty of Science of Rabat, Mohammed V University, 4 Avenue Ibn Battouta B. P. 1014 RP, Rabat, Morocco
Said Chah Slaoui: Department of Computer Science, Faculty of Science of Rabat, Mohammed V University, 4 Avenue Ibn Battouta B. P. 1014 RP, Rabat, Morocco

Journal of Information & Knowledge Management (JIKM), 2018, vol. 17, issue 02, 1-18

Abstract: Recently, MapReduce-based implementations of clustering algorithms have been developed to cope with the Big Data phenomenon, and they show promising results particularly for the document clustering problem. In this paper, we extend an efficient data partitioning method based on the relational analysis (RA) approach and applied to the document clustering problem, called PDC-Transitive. The introduced heuristic is parallelised using the MapReduce model iteratively and designed with a single reducer which represents a bottleneck when processing large data, we improved the design of the PDC-Transitive method to avoid the data dependencies and reduce the computation cost. Experiment results on benchmark datasets demonstrate that the enhanced heuristic yields better quality results and requires less computing time compared to the original method.

Keywords: Document clustering; hard clustering; Hadoop; MapReduce; partitioning heuristic; relational analysis approach; unsupervised clustering (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219649218500211

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