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Using Latent Semantic Indexing to Improve the Accuracy of Document Clustering

Author

Listed:
  • Jiaming Zhan

    (Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore)

  • Han Tong Loh

    (Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore)

Abstract

Document clustering is a significant research issue in information retrieval and text mining. Traditionally, most clustering methods were based on the vector space model which has a few limitations such as high dimensionality and weakness in handling synonymous and polysemous problems. Latent semantic indexing (LSI) is able to deal with such problems to some extent. Previous studies have shown that using LSI could reduce the time in clustering a large document set while having little effect on clustering accuracy. However, when conducting clustering upon a small document set, the accuracy is more concerned than efficiency. In this paper, we demonstrate that LSI can improve the clustering accuracy of a small document set and we also recommend the dimensions needed to achieve the best clustering performance.

Suggested Citation

  • Jiaming Zhan & Han Tong Loh, 2007. "Using Latent Semantic Indexing to Improve the Accuracy of Document Clustering," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 181-188.
    • Handle: RePEc:wsi:jikmxx:v:06:y:2007:i:03:n:s0219649207001755
    DOI: 10.1142/S0219649207001755
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    Cited by:

    1. Saida Ishak Boushaki & Nadjet Kamel & Omar Bendjeghaba, 2018. "High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-24, September.

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