Users Constraints in Itemset Mining
This work addresses a practical need in data mining for more flexible querying, though it appears incremental as it extends existing constraint programming approaches to include dataset constraints.
The paper tackles the problem of handling user constraints on both items and the dataset in itemset mining, presenting a general constraint programming model that can efficiently answer any such queries.
Discovering significant itemsets is one of the fundamental problems in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks. With a constraint programming approach, we can easily express and efficiently answer queries with users constraints on items. However, in many practical cases it is possible that queries also express users constraints on the dataset itself. For instance, asking for a particular itemset in a particular part of the dataset. This paper presents a general constraint programming model able to handle any kind of query on the items or the dataset for itemset mining.