A Data Science Model for Big Data Analytics of Frequent Patterns
Carson Kai-Sang Leung, Fan Jiang, Hao Zhang, Adam G.M. Pazdor · 2016
Frequent pattern mining is an important data mining task. Since its introduction, it has drawn attention from many researchers. Consequently, many frequent pattern mining algorithms have been proposed, which include level-wise Apriori-based algorithms, tree-based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and benefit from a few advantages, they also suffer from some disadvantages. In the current era of big data, a wide variety of high volumes of valuable data of different veracities can be easily collected and generated at a high velocity. These big data lead to additional challenges for frequent pattern mining. In this paper, we present a data science model for big data analytics of frequent patterns with MapReduce. We evaluated our model by using social networks, which are good examples of big data. Evaluation results show the efficiency and practicality of our data science model in mining and analyzing big data for the discovery of interesting frequent patterns from various real-life applications including social network analysis.