Title page for etd-0718100-165257


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URN etd-0718100-165257
Author Yuan-Xin Dong
Author's Email Address No Public.
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Department Information Management
Year 1999
Semester 2
Degree Master
Type of Document
Language English
Title Mining-Based Category Evolution for Text Databases
Date of Defense 2000-07-14
Page Count 68
Keyword
  • Text Categorization
  • Clustering
  • Category management
  • Category Evolution
  • Abstract As text repositories grow in number and size and global connectivity improves, the amount of online information in the form of free-format text is growing extremely rapidly. In many large organizations, huge volumes of textual information are created and maintained, and there is a pressing need to support efficient and effective information retrieval, filtering, and management. Text categorization is essential to the efficient management and retrieval of documents. Past research on text categorization mainly focused on developing or adopting statistical classification or inductive learning methods for automatically discovering text categorization patterns from a training set of manually categorized documents. However, as documents accumulate, the pre-defined categories may not capture the characteristics of the documents. In this study, we proposed a mining-based category evolution (MiCE) technique to adjust the categories based on the existing categories and their associated documents. According to the empirical evaluation results, the proposed technique, MiCE, was more effective than the discovery-based category management approach, insensitive to the quality of original categories, and capable of improving classification accuracy.
    Advisory Committee
  • Fu-Ren Lin - chair
  • Nian-Shing Chen - advisor
  • Chih-Ping Wei - advisor
  • Files
  • master_thesis_v11.pdf
  • indicate access worldwide
    Date of Submission 2000-07-18

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