Interactive constrained frequent-pattern mining system

Data mining refers to the search for implicit, previously unknown, and potentially useful information (such as frequent patterns) that might be embedded in data. Most of the existing data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, data mining is supposed to be an exploratory process. In this context, we are working on a project with the objective of implementing an efficient, interactive, human-centered system for mining frequent patterns that satisfy the user constraints. In this paper, we develop such a system, called iCFP, for interactive mining of Constrained Frequent Patterns. Our developed system uses a tree-based mining framework. In addition, it (i) allows human users to impose a certain focus on the mining process, (ii) provides users with feedback during the mining process, and (iii) permits users to dynamically change their constraints during the process.