English, Article edition: Using Latent Semantic Indexing to Improve the Accuracy of Document Clustering Jiaming Zhan; Han Tong Loh

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Physical Description
  • article
Language
  • English

Edition details

Title
  • Using Latent Semantic Indexing to Improve the Accuracy of Document Clustering
Author
  • Jiaming Zhan
  • Han Tong Loh
Physical Description
  • article
Notes
  • 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.
  • Latent semantic indexing, vector space model, document clustering, information retrieval, text mining
  • RePEc:wsi:jikmxx:v:06:y:2007:i:03:p:181-188
Language
  • English
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