Using Latent Semantic Indexing to Improve the Accuracy of Document Clustering

Jiaming Zhan () and Han Tong Loh
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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

Journal of Information & Knowledge Management (JIKM), 2007, vol. 06, issue 03, 181-188

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.

Keywords: Latent semantic indexing; vector space model; document clustering; information retrieval; text mining (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219649207001755

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