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.
Impact and interest:
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|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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