ORIGAMI: A novel and effective approach for mining representative orthogonal graph patterns

Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Jeremy Besson, Mohammed J. Zaki

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

In this paper, we introduce the concept of α-orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are α-orthogonal if their similarity is bounded above by α. Each α-orthogonal pattern is also a representative for those patterns that are at least β similar to it. Given user defined α, β ∈ [0,1], the goal is to mine an α-orthogonal, β-representative set that minimizes the set of unrepresented patterns. We present ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns. ORIGAMI first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. ORIGAMI then extracts an α-orthogonal, β-representative set from the mined maximal patterns.We show the effectiveness of our algorithm on a number of real and synthetic datasets. In particular, we show that our method is able to extract high-quality patterns even in cases where existing enumerative graph mining methods fail to do so.

Original languageEnglish
Pages (from-to)67-84
Number of pages18
JournalStatistical Analysis and Data Mining
Volume1
Issue number2
DOIs
Publication statusPublished - 1 Jun 2008

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Keywords

  • Graph mining
  • Maximal pattern mining
  • Randomized algorthims

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

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