Title :
Finding Short Patterns to Classify Text Documents
Author :
An, Jiyuan ; Chen, Yi-Ping Phoebe
Author_Institution :
Sch. of Inf. Technol., Deakin Univ., Geelong, Vic.
Abstract :
Many classification methods have been proposed to find patterns in text documents. However, according to Occam\´s razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
Keywords :
classification; text analysis; tree searching; Occam razor principle; breadth-first algorithm; depth-first pattern generation algorithm; text document classification; text document pattern finding; Australia; Equations; Information technology; Robustness; Support vector machine classification; Support vector machines; Test pattern generators; Testing; Text categorization; Document Categorization; breadth-first; depth-first.; rule generation;
Conference_Titel :
Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2747-7