A Business Intelligence Solution for Frequent Pattern Mining on Social Networks
Fan Jiang, Carson Kai-Sang Leung · 2014
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 this paper, we propose and evaluate an alternative frequent pattern mining algorithm called B-mine. Evaluation results show that our proposed algorithm is both space- and time-efficient. Furthermore, to show the practicality of B-mine in real-life applications, we apply B-mine to discover frequent following patterns in social networks.