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A parallel text document clustering algorithm based on neighbors

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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.

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Authors and Affiliations

  1. Department of Computer and Information Science, Fordham University, Bronx, NY, 10458, USA

    Yanjun Li

  2. Teradata Corporation, San Diego, CA, 92127, USA

    Congnan Luo

  3. Department of Computer Science and Engineering, Wright State University, Dayton, OH, 45435, USA

    Soon M. Chung

Authors
  1. Yanjun Li
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  2. Congnan Luo
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  3. Soon M. Chung
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Correspondence to Soon M. Chung.

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Li, Y., Luo, C. & Chung, S.M. A parallel text document clustering algorithm based on neighbors. Cluster Comput 18, 933–948 (2015). https://doi.org/10.1007/s10586-015-0450-z

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