Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 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
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Incremental Mining of Constrained Associations
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Single pass construction process of the CanTree for deriving association rules has been attracting researchers for data mining and incremental data mining to accommodate growth of transactional logs. This paper proposes five step mechanism for building a CanTree in HPC. The Pima Indian Diabetes Data Set considered for demonstrating a proposed mechanism and its performance.