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Corresponding Author

El-Shafaiy, Engy

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

Big Data" connects large-volume, complex, and increasing data sets with multiple independent sources. Nowadays, Big Data are speedily expanding in all science and engineering domains due to the rapid evolution of data, data storage, and the networking collection capabilities. Due to its variability, volume, and velocity, "Big Data mining" enjoys the ability of extracting constructive information from huge streams of data or datasets. Data mining includes exploring and analyzing big quantities of data in order to locate different molds for big data. "Frequent item sets Mining" is one of the most important tasks for discovering useful and meaningful patterns from large collections of data. Mining of association rules from frequent patterns from big data mining is of interest for many industries, for it can provide guidance in decision making processes; such as cross marketing, market basket analysis, promotion assortment, ...etc. The techniques of discovering association rules from data have traditionally focused on identifying the relationship between items predicting some aspect of human behavior; usually buying behavior. This paper provides a review on different techniques for mining frequent item sets.

Keywords

Association Rule Mining; Data mining; Frequent Item sets; Big Data; Frequent Pattern Mining; Apriori; FP-Growth

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