Frequent pattern mining in mobile devices: A feasibility study

Abstract

The availability of computational power in mobile devices is key-enabler for Mobile Data Mining (MDM) at user-premises. Alternately, resource-constraints like limited energy, narrow bandwidth, and small screens challenge in adoption of MDM. Currently, MDM is based on light-weight algorithms that are adaptive in resource-constrained environments but a study to evaluate the performance of general algorithms still lacks in the literature. To this end, we have studied six Frequent Pattern Mining (FPM) algorithms and deployed them in mobile devices to evaluate the feasibility and highlighted the associated challenges. The experiments were performed on real and synthetic data sets strictly in android-based mobile device and compared with PC-based setup. The experimental results show that FPM algorithms can leverage MDM after tuning some basic parameters.