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Document overlapping clustering using formal concept analysis

Author

Listed:
  • Yi-Hui Chen

    (Department of Applied Informatics and Multimedia, Asia University, Taiwan)

  • Eric Jui-Lin Lu

    (Department of Management Information Systems, National Chung Hsing University, Taiwan)

  • Yu-Ting Lin

    (Department of Management Information Systems, National Chung Hsing University, Taiwan)

  • Ya-Wen Cheng

    (Department of Management Information Systems, National Chung Hsing University, Taiwan)

Abstract

Text document clustering is a technique which groups documents into several clusters based on the similarities among documents. Most clustering algorithms build disjoint clusters, but clusters should be overlapped because documents may belong to two or more categories in real world. For example, an article discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. In this paper, we propose an overlapping clustering algorithm by using the Formal Concept Analysis, which could make a document assigned to two or more clusters. Moreover, our algorithm reduced the dimensions of the vector space, and performed more efficiently than existing clustering methods.

Suggested Citation

  • Yi-Hui Chen & Eric Jui-Lin Lu & Yu-Ting Lin & Ya-Wen Cheng, 2016. "Document overlapping clustering using formal concept analysis," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(2), pages 28-34.
    • Handle: RePEc:apb:jaterr:2016:p:28-34
    DOI: 10.20474/jater-2.2.1
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    References listed on IDEAS

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    1. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
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