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Title:Mining sophisticated patterns for classification and correlation analysis
Author(s):Kim, Sangkyum
Director of Research:Han, Jiawei
Doctoral Committee Chair(s):Han, Jiawei
Doctoral Committee Member(s):Zhai, ChengXiang; Chang, Kevin C-C.; Schatz, Bruce R.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2012-02-01
URI:http://hdl.handle.net/2142/29451
Rights Information:Copyright 2011 Sangkyum Kim
Date Available in IDEALS:2014-02-01
Date Deposited:2011-12


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