Combining preference- and content-based approaches for improving document clustering effectiveness

E-commerce and knowledge management applications generate and consume tremendous amounts of online information that is typically available as textual documents. To facilitate subsequent access of and leverage from these textual documents, the efficient and effective management of the ever-increasing volume of documents is essential to both organizations and individuals. Document management practices suggest the popularity of using categories (e.g., folders) for organizing, archiving, and accessing documents. Document clustering represents an appealing approach to enable organizations or individuals to create and maintain document categories automatically. Existing document clustering techniques usually group together similar documents on the basis of their textual content similarity. However, such content-based approaches operate at the lexical level and suffer greatly from the word mismatch problem. Therefore, this study aims to address this problem by exploiting users

• Type: Journal
• Year: 2006
• Where: IPM
• Authors: Chih-Ping Wei, Chin-Sheng Yang, Han-Wei Hsiao, Tsang-Hsiang Cheng
• Keywords: Content-based Approaches, Document, IPM 2006, Textual Documents