Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach
This work addresses the need for flexible data mining to extract both frequent and rare patterns, though it appears incremental as it applies constraint programming to an existing problem.
The paper tackles the problem of mining frequent itemsets including rare ones by proposing a constraint programming approach that allows flexible specification of multiple minimum supports, and experimental results show its practical effectiveness compared to state-of-the-art methods.
The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical effectiveness of our approach compared to the state of the art.