Mining approximate frequent patterns from graph databases

Authors
Anchuri, Pranay
ORCID
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Other Contributors
Zaki, Mohammed J., 1971-
Magdon-Ismail, Malik
Goldberg, Mark
Ravichandran, T.
Issue Date
2015-08
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Approximate Patterns : We propose three models for approximate matching of patterns in a given input graph. First, we allow for bounded label mismatches of the pattern. To find these approximate matches, we proposed a neighborhood-label based algorithm that can effciently prune infeasible matches of the pattern. Second, we allow both bounded label and structural mismatches of the pattern. To effciently find such approximate matches, we repeatedly use our label-only algorithm on a specially constructed subgraph of the pattern. Finally, we discuss why the existing models cannot be adapated to mine interesting patterns from uncertain graphs where edges are associated with a probability of existence. Therefore, we propose coverage based pattern mining that is a novel way to think about pattern mining in uncertain graphs. Our algorithm essentially enumerates a set of patterns that covers distinct regions of the input graph with high probability.
Description
August 2015
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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