Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 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
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and 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
Efficient incremental maintenance of frequent patterns with FP-tree
Journal of Computer Science and Technology
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Incremental updating of frequent item-sets on a database includes three problems. In this paper, these problems are explored when database stores massive data. The main contributions include: (a) introduces the concept of Interesting Support Threshold; (b) proposes Frequent Item-sets Tree (FITr) with compact structure; (c) proposes and implements algorithm FIIU for frequent item-sets incremental updating; (d) in order to further improve performance, proposes the algorithm DFIIU for distributed incremental updating of frequent Item-sets on massive database; (e) gives extensive experiments to show that FIIU and DFIIU algorithms have better performance than traditional algorithm on massive database when the number of items is less.