Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Mining mobility user profiles for car pooling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
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We apply techniques that originate in the analysis of market basket data sets to the study of frequent trajectories in graphs. Trajectories are defined as simple paths through a directed graph, and we put forth some definitions and observations about the calculation of supports of paths in this context. A simple algorithm for calculating path supports is introduced and analyzed, but we explore an algorithm which takes advantage of traditional frequent item set mining techniques, as well as constraints placed on supports by the graph structure, for optimizing the calculation of relevant supports. To this end, the notion of the path tree is introduced, as well as an algorithm for producing such path trees.