A fine grained heuristic to capture web navigation patterns
ACM SIGKDD Explorations Newsletter
Mining patterns from graph traversals
Data & Knowledge Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
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
Extension of Graph-Based Induction for General Graph Structured Data
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
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
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
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
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Frequent Pattern Discovery from a Single Graph with Quantitative Itemsets
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
WTPMiner: efficient mining of weighted frequent patterns based on graph traversals
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
How to use "classical" tree mining algorithms to find complex spatio-temporal patterns?
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Finding itemset-sharing patterns in a large itemset-associated graph
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Directed acyclic graphs can be used across many application domains. In this paper, we study a new pattern domain for supporting their analysis. Therefore, we propose the pattern language of weighted paths, primitive constraints that enable to specify their relevancy (e.g., frequency and compactness constraints), and algorithms that can compute the specified collections. It leads to a condensed representation setting whose efficiency and scalability are empirically studied.