Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
SEuS: Structure Extraction Using Summaries
DS '02 Proceedings of the 5th International Conference on Discovery Science
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
GREW-A Scalable Frequent Subgraph Discovery Algorithm
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
MARGIN: Maximal Frequent Subgraph Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fg-index: towards verification-free query processing on graph databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ORIGAMI: Mining Representative Orthogonal Graph Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient topological OLAP on information networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Gelling, and melting, large graphs by edge manipulation
Proceedings of the 21st ACM international conference on Information and knowledge management
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Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-specified constraints cannot be handled in a way that is direct and precise. In this paper, we propose a direct mining framework to solve the problem and illustrate our ideas in the context of a particular type of constrained frequent patterns --- the "skinny" patterns, which are graph patterns with a long backbone from which short twigs branch out. These patterns, which we formally define as l-long δ-skinny patterns, are able to reveal insightful spatial and temporal trajectory patterns in mobile data mining, information diffusion, adoption propagation, and many others. Based on the key concept of a canonical diameter, we develop SkinnyMine, an efficient algorithm to mine all the l-long δ-skinny patterns guaranteeing both the completeness of our mining result as well as the unique generation of each target pattern. We also present a general direct mining framework together with two properties of reducibility and continuity for qualified constraints. Our experiments on both synthetic and real data demonstrate the effectiveness and scalability of our approach.