NDLI: Feature discovery in relevance feedback using pattern mining

Content Provider	IEEE Xplore Digital Library
Author	Pipanmaekaporn, L.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. & Inf. Sci., King Mongkut's Univ. of Technol. North Bangkok, Bangkok, Thailand (Pipanmaekaporn, L.)
Abstract	It is a big challenge to guarantee the quality of extracted features in text documents to describe user interests or preferences due to large amounts of noise. Over the years, pattern mining-based approaches to RF have attracted great interest to discover knowledge of user interest from text documents. However, the data mining approaches often produce a large set of patterns, which include a lot of noisy patterns, reducing the effective use of pattern mining. In this paper, we present a novel pattern mining approach to RF. This approach mines patterns in both positive and negative feedback and then classifies them into clusters to find user-specific patterns. We also propose a novel pattern deploying method that effectively uses the discovered patterns for improving the performance of searching relevant documents. Experiments are conducted on Reuters Corpus Volume 1 data collection (RCV1) and TREC filtering topics. The results show that the proposed approach achieves promising performance comparing with state-of-the-art term-based methods and pattern-based ones.
Starting Page	301
Ending Page	307
File Size	867879
Page Count	7
File Format	PDF
ISBN	9781479901746
DOI	10.1109/ICIS.2013.6607858
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Data mining Radio frequency Training Noise measurement Data collection Classification algorithms Pattern Mining Relevance Feedback Relevant Feature Extraction Pattern Cleaning
Content Type	Text
Resource Type	Article

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