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  1. Proceedings of the 7th Forum for Information Retrieval Evaluation (FIRE '15)
  2. Context-driven Dimensionality Reduction for Clustering Text Documents
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Context-driven Dimensionality Reduction for Clustering Text Documents
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Dimensionality Reduction and Clustering of Text Documents

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Context-driven Dimensionality Reduction for Clustering Text Documents

Content Provider ACM Digital Library
Author Ganguly, Debasis Jones, Gareth J. F. Leveling, Johannes
Abstract We investigate clustering documents based on automatically annotated potentially sensitive information extracted from a large collection of organizational data. The process of clustering in this particular use case is helpful to visualize and navigate through groups of documents with related content. However, the effectiveness and efficiency of document clustering is limited mainly due to the large dimensionality of the document vectors. To alleviate this problem we propose a dimensionality reduction approach which involves selecting terms with high tf-idf scores from the context of the automatically annotated sensitive regions of a document. Due to the unavailability of real organizational data for research purposes, we evaluate our approach on the standard 20 news-groups dataset. For evaluation purposes, the only sensitive information that we use from the documents of this dataset are the named entities, e.g. the names of persons and organizations. Experimental results show that our approach is able to achieve an almost perfect clustering with a purity value of 0.998 improving by 22.60% with respect to the purity value of 0.814 obtained without document dimensionality reduction.
Starting Page 1
Ending Page 7
Page Count 7
File Format PDF
ISBN 9781450340045
DOI 10.1145/2838706.2838708
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-12-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Dimensionality reduction Document clustering
Content Type Text
Resource Type Article
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