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SIGMOD
2008
ACM

Mining significant graph patterns by leap search

10 years 11 months ago
Mining significant graph patterns by leap search
With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. Most objective functions are not antimonotonic, which could fail all of frequency-centric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP(Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable ...
Xifeng Yan, Hong Cheng, Jiawei Han, Philip S. Yu
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2008
Where SIGMOD
Authors Xifeng Yan, Hong Cheng, Jiawei Han, Philip S. Yu
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