Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th 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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On condensed representations of constrained frequent patterns
Knowledge and Information Systems
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Mining frequent itemsets has been one of the hot topics in data mining. Candidate generation-and-test approaches such as Apriori have been proved to be effective. However, in practical applications, we will face a lot of intractable frequent itemsets under the preset minimum support. In order to solve the problem, we have two methods: constraint-based mining and frequent closed itemsets mining. To the best of our knowledge, it has not been studied how to combine one of the complex constraint, tough constraint, with the frequent closed itemsets mining. In this paper, we show the benefits of combining the two technologies through the TC-based FCM Algorithm. We also discuss the following two problems: 1) which one should be put in advance, select process or filter process? 2) how to make full use of the information from the upper level.