ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns

: In this paper, we introduce the concept of -orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are -orthogonal if their similarity is bounded above by . Each -orthogonal pattern is also a representative for those patterns that are at least similar to it. Given user defined , [0, 1], the goal is to mine an -orthogonal, -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 -orthogonal, -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 enumerativ...

• Type: Journal
• Year: 2008
• Where: SADM
• Authors: Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Jérémy Besson, Mohammed J. Zaki
• Keywords: -orthogonal Pattern, Graph Patterns, Maximal Patterns, SADM 2008