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Cluster Computing
Erschienen in: Cluster Computing 2/2015

01.06.2015

A parallel text document clustering algorithm based on neighbors

verfasst von: Yanjun Li, Congnan Luo, Soon M. Chung

Erschienen in: Cluster Computing | Ausgabe 2/2015

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Abstract

In this paper, we propose a new parallel algorithm for text document clustering based on the concept of neighbor (Guha et al. in Inf Syst 25(5):345–366, 2000). If two documents are similar enough, they are considered as neighbors of each other. The new algorithm is named parallel k-means based on neighbors (PKBN), and it is a parallel version of sequential k-means based on neighbors (SKBN) that we proposed in Luo et al. (Data Knowl Eng 68(11):1271–1288, 2009). PKBN fully exploits the data-parallelism of SKBN and adopts a new parallel pair-generating method to build the neighbor matrix. Our new parallel pair-generating method causes less communication overhead between processors than existing methods. PKBN is designed for message-passing multiprocessor systems and is implemented on a cluster of Linux workstations to analyze its performance. Our experimental results on real-life data sets demonstrate that PKBN is very efficient and has good scalability with respect to the number of processors and the size of data set.
Vorheriger Artikel Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure
Nächster Artikel Achieving self-aware parallelism in stream programs

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Metadaten
Titel
A parallel text document clustering algorithm based on neighbors
verfasst von
Yanjun Li
Congnan Luo
Soon M. Chung
Publikationsdatum
01.06.2015
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2015
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-015-0450-z

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