Director of Research (if dissertation) or Advisor (if thesis)
Chang, Kevin C-C.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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