University of Illinois Urbana-Champaign

Mining sophisticated patterns for classification and correlation analysis

Kim, Sangkyum

Content Files
Kim_Sangkyum.pdf

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https://hdl.handle.net/2142/29451

Description

Title
Mining sophisticated patterns for classification and correlation analysis
Author(s)
Kim, Sangkyum
Issue Date
2012-02-01T00:46:55Z
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Doctoral Committee Chair(s)
Han, Jiawei
Committee Member(s)
  • Zhai, ChengXiang
  • Chang, Kevin C-C.
  • Schatz, Bruce R.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2012-02-01T00:46:55Z
Keyword(s)
  • sophisticated pattern mining
  • k-embedded-edge subtree
  • discriminative pattern
  • correlated pattern
  • flipping correlation pattern
Abstract
Pattern mining has been a hot issue since it was first proposed for market basket analysis. Even though pattern mining is one of the oldest topic in data mining domain, there are still many ongoing challenges to overcome on this subject since the scale of the data size is getting bigger and the complexity of data structure is getting more complicated. This dissertation discusses several pattern mining tasks, challenges associated with them, and algorithm designs that overcome these challenges. Specifically, we design and implement techniques for (1) directly mining discriminative patterns from a numeric valued feature set of k-embedded edge subtrees given labeled training data, (2) mining top correlated patterns from transactional databases with low minimum support, and (3) mining flipping correlation patterns from transactional databases given item hierarchy. We evaluate our solutions by conducting comprehensive experiments on large-scale synthetic and real world datasets.
Graduation Semester
2011-12
Permalink
http://hdl.handle.net/2142/29451
Copyright and License Information
Copyright 2011 Sangkyum Kim

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