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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0718100-165257 |
| Date | 18 July 2000 |
| Creators | Dong, Yuan-Xin |
| Contributors | Fu-Ren Lin, Nian-Shing Chen, Chih-Ping Wei |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0718100-165257 |
| Rights | unrestricted, Copyright information available at source archive |
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