Mining Frequent delta-Free Patterns in Large Databases

Mining patterns under constraints in large data (also called fat data) is an important task to benefit from the multiple uses of the patterns embedded in these data sets. It is a difficult task due to the exponential growth of the search space according to the number of attributes. From such contexts, closed patterns can be extracted by using the properties of the Galois connections. But, from the best of our knowledge, there is no approach to extract interesting patterns like δ-free patterns which are on the core of a lot of relevant rules. In this paper, we propose a new method based on an efficient way to compute the extension of a pattern and a pruning criterion to mine frequent δ-free patterns in large databases. We give an algorithm (FTminer) for the practical use of this method. We show the efficiency of this approach by means of experiments on benchmarks and on gene expression data.

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