article

Accommodating Individual Preferences in the Categorization of Documents: A Personalized Clustering Approach

Authors:

Roger Chiang, and

Chia-Chen WuAuthors Info & Claims

Journal of Management Information Systems, Volume 23, Issue 2

Pages 173 - 201

https://doi.org/10.2753/MIS0742-1222230208

Published: 01 October 2006 Publication History

Abstract

As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, individuals and organizations frequently organize their documents into categories that facilitate document management and subsequent access and browsing. Document clustering is an intentional act that should reflect individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. In this paper, we present an automatic document-clustering approach that incorporates an individual's partial clustering as preferential information. Combining two document representation methods, feature refinement and feature weighting, with two clustering methods, precluster-based hierarchical agglomerative clustering (HAC) and atomic-based HAC, we establish four personalized document-clustering techniques. Using a traditional content-based document-clustering technique as a performance benchmark, we find that the proposed personalized document-clustering techniques improve clustering effectiveness, as measured by cluster precision and cluster recall.

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Index Terms

Accommodating Individual Preferences in the Categorization of Documents: A Personalized Clustering Approach
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Management Information Systems

Journal of Management Information Systems Volume 23, Issue 2

Number 2 / October 2006

290 pages

ISSN:0742-1222

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M. E. Sharpe, Inc.

United States

Publication History

Published: 01 October 2006

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https://dl.acm.org/doi/10.1007/s00799-023-00389-8
Watanabe SIto HMatsubara MMorishima A(2022)Bibrecord-Based Literature Management with Interactive Latent Space LearningFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_13(155-171)Online publication date: 30-Nov-2022
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