Fuzzy orderings for fuzzy gradual patterns
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Mining high coherent association rules with consideration of support measure
Expert Systems with Applications: An International Journal
Para Miner: a generic pattern mining algorithm for multi-core architectures
Data Mining and Knowledge Discovery
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Numerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: "When X increases, Y decreases". There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual item sets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns.