ORIGAMI: Mining Representative Orthogonal Graph Patterns
Al Hasan, M.
Chaoji, V.
Salem, S.
Besson, J.
Zaki, M.J.
Rensselaer Polytech. Inst., Troy;
This paper appears in: Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
Publication Date: 28-31 Oct. 2007
On page(s): 153-162
Location: Omaha, NE,
ISSN: 1550-4786
ISBN: 978-0-7695-3018-5
INSPEC Accession Number: 9894644
Digital Object Identifier: 10.1109/ICDM.2007.45
Current Version Published: 2008-03-12
Abstract
In this paper, we introduce the concept of alpha-orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are alpha-orthogonal if their similarity is bounded above by alpha. Each alpha-orthogonal pattern is also a representative for those patterns that are at least beta similar to it. Given user defined alpha, beta isin [0,1], the goal is to mine an alpha-orthogonal, beta-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 alpha-orthogonal, beta-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.
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