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ABSTRACT

In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. Currently, what the state-of-art techniques give is a lengthy list of exact patterns, which are undesirable in the following two aspects: (1) on the micro side, due to various inherent noises or data diversity, exact patterns are usually not too useful in many real applications; and (2) on the macro side, the rigid structural requirement being posed often generates an excessive amount of patterns that are only slightly different from each other, which easily overwhelm the users.

In this paper, we study the presentation problem of graph patterns, where structural representatives are deemed as the key mechanism to make the whole strategy effective. As a solution to fill the usability gap, we adopt a two-step smoothing-clustering framework, with the first step adding error tolerance to individual patterns (the micro side), and the second step reducing output cardinality by collapsing multiple structurally similar patterns into one representative (the macro side). This novel, integrative approach is never tried in previous studies, which essentially rolls-up our attention to a more appropriate level that no longer looks into every minute detail. The above framework is general, which may apply under various settings and incorporate a lot of extensions. Empirical studies indicate that a compact group of informative delegates can be achieved on real datasets and the proposed algorithms are both efficient and scalable.

REFERENCES

• 1: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 2: Roberto J. Bayardo, Jr., Efficiently mining long patterns from databases, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.85-93, June 01-04, 1998, Seattle, Washington, United States
• 3: Christian Borgelt , Michael R. Berthold, Mining Molecular Fragments: Finding Relevant Substructures of Molecules, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.51, December 09-12, 2002
• 4: Chen Chen , Xifeng Yan , Feida Zhu , Jiawei Han, gApprox: Mining Frequent Approximate Patterns from a Massive Network, Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, p.445-450, October 28-31, 2007 [doi>10.1109/ICDM.2007.36]
• 5: Mukund Deshpande , Michihiro Kuramochi , Nikil Wale , George Karypis, Frequent Substructure-Based Approaches for Classifying Chemical Compounds, IEEE Transactions on Knowledge and Data Engineering, v.17 n.8, p.1036-1050, August 2005 [doi>10.1109/TKDE.2005.127]
• 6: Jiawei Han , Jianyong Wang , Ying Lu , Petre Tzvetkov, Mining Top.K Frequent Closed Patterns without Minimum Support, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.211, December 09-12, 2002
• 7: Mohammad Al Hasan , Vineet Chaoji , Saeed Salem , Jeremy Besson , Mohammed J. Zaki, ORIGAMI: Mining Representative Orthogonal Graph Patterns, Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, p.153-162, October 28-31, 2007 [doi>10.1109/ICDM.2007.45]
• 8: Dorit S. Hochbaum, Approximation algorithms for NP-hard problems, PWS Publishing Co., Boston, MA, 1996
• 9: Jun Huan , Wei Wang , Jan Prins , Jiong Yang, SPIN: mining maximal frequent subgraphs from graph databases, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014123]
• 10: L. Kaufman and P. J. Rousseeuw, editors. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley and Sons, 1990.
• 11: Michihiro Kuramochi , George Karypis, Frequent Subgraph Discovery, Proceedings of the 2001 IEEE International Conference on Data Mining, p.313-320, November 29-December 02, 2001
• 12: M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. In SDM, 2004.
• 13: Y. Liu, J. Li, and H. Gao. Summarizing graph patterns. In ICDE, pages 903--912, 2008.
• 14: T. Mielikäinen and H. Mannila. The pattern ordering problem. In PKDD, pages 327--338, 2003.
• 15: Raymond T. Ng , Jiawei Han, CLARANS: A Method for Clustering Objects for Spatial Data Mining, IEEE Transactions on Knowledge and Data Engineering, v.14 n.5, p.1003-1016, September 2002 [doi>10.1109/TKDE.2002.1033770]
• 16: Siegfried Nijssen , Joost N. Kok, A quickstart in frequent structure mining can make a difference, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014134]
• 17: Nicolas Pasquier , Yves Bastide , Rafik Taouil , Lotfi Lakhal, Discovering Frequent Closed Itemsets for Association Rules, Proceedings of the 7th International Conference on Database Theory, p.398-416, January 10-12, 1999
• 18: R. Sharan, S. Suthram, R. M. Kelley, T. Kuhn, S. McCuine, P. Uetz, T. Sittler, R. M. Karp, and T. Ideker. Conserved patterns of protein interaction in multiple species. PNAS, 102(6):1974--1979, 2005.
• 19: Chao Wang , Srinivasan Parthasarathy, Summarizing itemset patterns using probabilistic models, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150495]
• 20: Dong Xin , Jiawei Han , Xifeng Yan , Hong Cheng, Mining compressed frequent-pattern sets, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway
• 21: Xifeng Yan , Hong Cheng , Jiawei Han , Dong Xin, Summarizing itemset patterns: a profile-based approach, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081907]
• 22: Xifeng Yan , Jiawei Han, CloseGraph: mining closed frequent graph patterns, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956784]
• 23: Xifeng Yan , Philip S. Yu , Jiawei Han, Graph indexing: a frequent structure-based approach, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France [doi>10.1145/1007568.1007607]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Algorithms

Keywords:
frequent graph pattern, smoothing-clustering, structural representative