gPrune: A Constraint Pushing Framework for Graph Pattern Mining

In graph mining applications, there has been an increasingly strong urge for imposing user-specified constraints on the mining results. However, unlike most traditional itemset constraints, structural constraints, such as density and diameter of a graph, are very hard to be pushed deep into the mining process. In this paper, we give the first comprehensive study on the pruning properties of both traditional and structural constraints aiming to reduce not only the pattern search space but the data search space as well. A new general framework, called gPrune, is proposed to incorporate all the constraints in such a way that they recursively reinforce each other through the entire mining process. A new concept, Pattern-inseparable Data-antimonotonicity, is proposed to handle the structural constraints unique in the context of graph, which, combined with known pruning properties, provides a comprehensive and unified classification framework for structural constraints. The exploration of these antimonotonicities in the context of graph pattern mining is a significant extension to the known classification of constraints, and deepens our understanding of the pruning properties of structural graph constraints.

[1]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[2]  Philip S. Yu,et al.  Graph indexing: a frequent structure-based approach , 2004, SIGMOD '04.

[3]  Chen Wang,et al.  Constraint-Based Graph Mining in Large Database , 2005, APWeb.

[4]  Joost N. Kok,et al.  A quickstart in frequent structure mining can make a difference , 2004, KDD.

[5]  Yanchun Zhang,et al.  Web Technologies Research and Development - APWeb 2005, 7th Asia-Pacific Web Conference, Shanghai, China, March 29 - April 1, 2005, Proceedings , 2005, APWeb.

[6]  Wojciech Szpankowski,et al.  An efficient algorithm for detecting frequent subgraphs in biological networks , 2004, ISMB/ECCB.

[7]  A. Butte,et al.  Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Ehud Gudes,et al.  Computing frequent graph patterns from semistructured data , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[9]  Jiawei Han,et al.  Mining closed relational graphs with connectivity constraints , 2005, 21st International Conference on Data Engineering (ICDE'05).

[10]  Andrew V. Goldberg,et al.  Finding a Maximum Density Subgraph , 1984 .

[11]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[12]  George Karypis,et al.  SLPMiner: an algorithm for finding frequent sequential patterns using length-decreasing support constraint , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[13]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[14]  Wei Wang,et al.  Efficient mining of frequent subgraphs in the presence of isomorphism , 2003, Third IEEE International Conference on Data Mining.

[15]  Francesco Bonchi,et al.  Pushing Tougher Constraints in Frequent Pattern Mining , 2005, PAKDD.

[16]  Jiawei Han,et al.  CloseGraph: mining closed frequent graph patterns , 2003, KDD '03.

[17]  Christian Borgelt,et al.  Mining molecular fragments: finding relevant substructures of molecules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[18]  Dino Pedreschi,et al.  ExAnte: Anticipated Data Reduction in Constrained Pattern Mining , 2003, PKDD.

[19]  J. Snoeyink,et al.  Mining Spatial Motifs from Protein Structure Graphs , 2003 .

[20]  Daniel Kifer,et al.  DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints , 2002, Data Mining and Knowledge Discovery.

[21]  Luc De Raedt,et al.  Constraint-Based Mining and Inductive Databases, European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers , 2005, Constraint-Based Mining and Inductive Databases.

[22]  Jianyong Wang,et al.  Efficient closed pattern mining in the presence of tough block constraints , 2004, KDD.

[23]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[24]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.

[25]  Jiong Yang,et al.  SPIN: mining maximal frequent subgraphs from graph databases , 2004, KDD.

[26]  Hendrik Blockeel,et al.  Knowledge Discovery in Databases: PKDD 2003 , 2003, Lecture Notes in Computer Science.

[27]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[28]  George Karypis,et al.  Frequent substructure-based approaches for classifying chemical compounds , 2003, IEEE Transactions on Knowledge and Data Engineering.

[29]  Wei Wang,et al.  Mining protein family specific residue packing patterns from protein structure graphs , 2004, RECOMB.

[30]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[31]  G. Karypis,et al.  Frequent sub-structure-based approaches for classifying chemical compounds , 2005, Third IEEE International Conference on Data Mining.

[32]  Jean-François Boulicaut,et al.  Constraint-Based Mining and Inductive Databases, Springer-Verlag LNCS Volume 3848 , 2005 .

[33]  Jian Pei,et al.  Mining constrained gradients in large databases , 2004, IEEE Transactions on Knowledge and Data Engineering.

[34]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[35]  Dino Pedreschi,et al.  ExAnte: a preprocessing method for frequent-pattern mining , 2005, IEEE Intelligent Systems.

[36]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.