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Title:CASM: searching context-aware sequential patterns iteratively
Author(s):Zhong, Hengzhi
Advisor(s):Chang, Kevin C-C.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Sequential pattern mining
opinion search
Abstract:Many applications are interested in mining context-aware sequential patterns such as opinions, common navigation patterns, and product recommendations. However, traditional sequential pattern mining algorithms are not effective to mine such patterns. We thus study the problem of searching context-aware patterns on the fly. As a solution, we presented a variable-order random walk as the ranking model and developed two efficient algorithms GraphCAP and R3CAP. To show the effectiveness and efficiency of our solution, we conducted extensive experiments on real dataset. Lastly, we applied our solution to support opinion search, a novel application that significantly differs from traditional opinion mining and retrieval.
Issue Date:2011-08-26
URI:http://hdl.handle.net/2142/26413
Rights Information:Copyright 2011 Hengzhi Zhong
Date Available in IDEALS:2011-08-26
2013-08-27
Date Deposited:2011-08


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