Tough constraint-based frequent closed itemsets mining

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.

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