Mining individualized context-dependent behavioral rules from smartphone data

摘要

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.