Delivering Scalable Frequent Pattern Mining for Non-Expert Data Miners

Abstract

As a popular data mining task, frequent pattern mining has been proven to be help- ful for non-experts. For example, mining frequent purchased products helps store managers increase sales. As another example, finding popular courses assists uni- versity administrators arrange courses to avoid schedule conflicts. However, many data mining researchers have focused on improving algorithmic efficiency, but have put less focus on providing non-experts with a system designed specifically for these non-experts. In my M.Sc. thesis, I propose such a system, called PatternShow, which consists of (i) a user-friendly frontend web interface along with a visualization tool called BundleVis to show effectively frequent patterns for non-expert miners and (ii) a cloud-enabled backend that offers scalable frequent pattern mining. Results of my user study show the effectiveness of PatternShow in delivering scalable frequent pattern mining for non-expert data miners.