Mining individualized context-dependent behavioral rules from smartphone data

Article type: Other

Authors: Sarker, Iqbal H.^{; *}

Affiliations: Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC-3122, Australia. E-mail: msarker@swin.edu.au

Correspondence: [*] Corresponding author. E-mail: msarker@swin.edu.au.

Abstract: This PhD research formulates the problem of mining contextual behavioral rules for individual users utilizing their smartphone data. In particular, we propose a dynamic time-series segmentation technique for behavioral data clustering, a machine learning rule-based classification technique for context-aware usage behavior modeling and prediction, and a recency-based rule learning approach for dynamic updating and management of the discovered behavioral rules, to achieve our goal.

Keywords: Context-aware computing, mobile data mining, machine learning, clustering, classification, associations, user behavior modeling, personalization, rule-based model, predictive analytics, smartphone & IoT services, intelligent systems

DOI: 10.3233/AIS-190514

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 4, pp. 369-370, 2019