Issue Number: Vol. 5, No. 3
Year of Publication: Apr - 2015
Page Numbers: 165-170
Authors: Rajeb Akram, Ben Hamadou Abdelmajid, Loukil Zied
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P001679


Data mining is a set of methods used in the process of KDD( Knowledge Discovery in Data) in order to distinguish relationships and unknown patterns in the data. Mining patterns is an interesting technique and is widely used in data mining; its objective is to find the patterns that appear frequently in a database. The sequence mining is the pattern discovery problem in the sequences. A declarative approach has been proposed to solve this problem in order to transform this problem to an SAT model. In this paper, we propose a CSP-based encoding for the problem of discovering frequents and closed patterns in a sequence. We show that is possible to employee constraint programming techniques for modeling and solving a wide variety of constraint-based item-set mining tasks, such as frequent, closed and maximal. Preliminary experiments show that the new formulation is competitive and can outperform the SAT based approach on the considered sequences.