Rule Discovery from Textual Data

Rule Discovery from Textual Data

Shigeaki Sakurai (Toshiba Corporation, Japan)
Copyright: © 2008 |Pages: 19
DOI: 10.4018/978-1-59904-373-9.ch006
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Abstract

This chapter introduces knowledge discovery methods based on a fuzzy decision tree from textual data. It argues that the methods extract features of the textual data based on a key concept dictionary, which is a hierarchical thesaurus, and a key phrase pattern dictionary, which stores characteristic rows of both words and parts of speech, and generate knowledge in the format of a fuzzy decision tree. It also discusses two application tasks. One is an analysis system for daily business reports and the other is an e-mail analysis system. Furthermore, the author hopes that the methods will provide new knowledge for researchers engaged in text mining studies, facilitating their understanding of the importance of the fuzzy decision tree in processing textual data.

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