Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets. But in real applications, different items may have different criteria to judge its importance. The support requirements should then vary with different items. In this paper, we provide another point of view about defining the minimum supports of itemsets when items have different minimum supports. The maximum constraint is used, which is well explained and may be suitable to some mining domains. We then propose a simple algorithm based on the Apriori approach to find the large-itemsets and association rules under this constraint. The proposed algorithm is easy and efficient when compared to Wang et al.'s under the maximum constraint. The numbers of association rules and large itemsets obtained by the proposed mining algorithm using the maximum constraint are also less than those using the minimum constraint. Whether to adopt the proposed approach thus depends on the requirements of mining problems. Besides, the granular computing technique of bit strings is used to speed up the proposed data mining algorithm.
Literature REF based on the Apriori approach to generate the large itemsets with multiple minimum supports under the maximum constraints.
Mining Association Rules with Multiple Minimum Supports Using Maximum Constraints
Int. J. Approx. Reason.
Int. J. Approx. Reason.
Mathematics Computer Science