Finding Short Patterns to Classify Text Documents

An, Jiyuan; Chen, Yi-Ping Phoebe

doi:10.1109/WI.2006.82

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Author(s)

An, Jiyuan

Chen, Yi-Ping Phoebe

Griffith University Author(s)

An, Jay

Year published

2006

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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 algorithmMany 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
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Conference Title

2006 IEEE / WIC / ACM International Conference on Web Intelligence (WI-2006)

DOI

https://doi.org/10.1109/WI.2006.82

Copyright Statement

© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Publication URI

http://hdl.handle.net/10072/25629

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Conference outputs