A data mining framework for relevance feature discovery

Pipanmaekaporn, Luepol (2013) A data mining framework for relevance feature discovery. PhD thesis, Queensland University of Technology.


This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

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247 since deposited on 25 Sep 2013
101 in the past twelve months

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ID Code: 62857
Item Type: QUT Thesis (PhD)
Supervisor: Li, Yuefeng & Geva, Shlomo
Keywords: Relevance Feature Extraction, Pattern Cleaning, Pattern Taxonomy Model, Information Retrieval, Pattern Mining, Pattern Deploying
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 25 Sep 2013 02:55
Last Modified: 09 Sep 2015 21:49

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