Data mining: concepts and techniques
Data mining: concepts and techniques
Introduction to Algorithms
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Computing Frequent Graph Patterns from Semistructured Data
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
Mining Frequent Labeled and Partially Labeled Graph Patterns
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Discovering Frequent Graph Patterns Using Disjoint Paths
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern-growth approach for document organization
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
MARGIN: Maximal frequent subgraph mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Semantically-guided clustering of text documents via frequent subgraphs discovery
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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In this paper we present an efficient algorithm, called DSPM, for mining all frequent subgraphs in large set of graphs. The algorithm explores the search space in a DFS fashion, while generating candidates in advance to each mining phase just like the Apriori algorithm does. It combines the candidate generation and anti monotone pruning into one efficient operation thanks to the unique mode of exploration. DSPM efficiently enumerates all frequent patterns by using diagonal search, which is a general scheme for designing effective algorithms for hard enumeration problems. Our experiments show that DSPM has better performance, from several aspects, than the current state of the art - gSpan algorithm.