Frequent Pattern Outlier Detection without Exhaustive Mining

International audienceOutlier detection consists in detecting anomalous observations from data. During the past decade, outlier detection methods were proposed using the concept of frequent patterns. Basically such methods require to mine all frequent patterns for computing the outlier factor of each transaction. This approach remains too expensive despite recent progress in pattern mining field. In this paper, we provide exact and approximate methods for calculating the frequent pattern outlier factor (FPOF) without extracting any pattern or by extracting a small sample. We propose an algorithm that returns the exact FPOF of each transaction without mining any pattern. Surprisingly, it works in polynomial time on the size of the dataset. We also present an approximate method where the end-user controls the maximum error on the estimated FPOF. An experimental study shows the interest of both methods for very large datasets where exhaustive mining fails to provide the exact solution. The accuracy of our approximate method outperforms the baseline approach for a same budget

ParaMiner: a Generic Pattern Mining Algorithm for Multi-Core Architectures

International audienceIn this paper, we present Para Miner which is a generic and parallel algorithm for closed pattern mining. Para Miner is built on the principles of pattern enumeration in strongly accessible set systems. Its efficiency is due to a novel dataset reduction technique (that we call EL-reduction), combined with novel technique for performing dataset reduction in a parallel execution on a multi-core architecture. We illustrate Para Miner's genericity by using this algorithm to solve three different pattern mining problems: the frequent itemset mining problem, the mining frequent connected relational graphs problem and the mining gradual itemsets problem. In this paper, we prove the soundness and the completeness of Para Miner. Furthermore, our experiments show that despite being a generic algorithm, Para Miner can compete with specialized state of the art algorithms designed for the pattern mining problems mentioned above. Besides, for the particular problem of gradual itemset mining, Para Miner outperforms the state of the art algorithm by two orders of magnitude