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ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns
Author(s) -
Chaoji Vineet,
Al Hasan Mohammad,
Salem Saeed,
Besson Jeremy,
J. Zaki Mohammed
Publication year - 2008
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10004
Subject(s) - traverse , graph , computer science , set (abstract data type) , data mining , bounded function , similarity (geometry) , algorithm , pattern recognition (psychology) , mathematics , theoretical computer science , artificial intelligence , image (mathematics) , geography , mathematical analysis , geodesy , programming language
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. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008