Bioinspired Metaheuristic based Big Data Uncertain Itemset Mining Framework

Abstract

Uncertain pattern mining is considered as an NP-Hard problem due to its complexity and its execution time consummation. The problem is amplified in the Big Data era. Thus, we need to use techniques that don’t require prior knowledge of the search space as the metaheuristics algorithms, which use natural theories based on randomness. This work deals with the uncertainty of data when extracting frequent patterns from big uncertain (probabilistic) Datasets (BDUPM for Big Data Uncertain Pattern Mining). In addition to that, the BDUPM task is addressed as a combinatorial optimization problem in this study. In fact, we proposed three metaheuristic-based algorithms that are inspired from the Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO) and Genetic Algorithms (GA), for the purpose of extracting unexpected useful frequent patterns that help to get useful pieces of information to make trusted decisions. The proposed algorithms MRPSO-UFIM, MRBSO-UFIM and MRGA-UFIM are employed with the MapReduce programming model in a parallel and distributed environment, and examined based on the number of frequent itemsets retrieved, and computational time. The experiments have shown the efficiency of our proposed solutions when tested with several uncertain datasets. Key words : Frequent Pattern Mining, Uncertain Data, Big Data, Particle Swarm Optimization, Genetic Algorithms, Bee Swarm Optimization.