Mobile Data Mining

This SpringerBrief presents a typical life-cycle of mobile data mining applications,including: data capturing and processing which determines what data tocollect, how to collect these data, and how to reduce the noise in the databased on smartphone sensors feature engineering which extracts andselects features to serve as the input of algorithms based on the collectedand processed data model and algorithm design In particular, this brief concentrateson the model and algorithm design aspect, and explains three challenging requirementsof mobile data mining applications: energy-saving, personalization, and real-time Energy saving is a fundamental requirement of mobile applications, due to thelimited battery capacity of smartphones. The authors explore the existingpractices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobileapplications is personalization. Most of the existing methods tend to train generic models for all users, but the authors provide existing personalizedtreatments for mobile applications, as the behaviors may differ greatly fromone user to another in many mobile applications. The third requirement isreal-time. That is, the mobile application should return responses in a real-timemanner, meanwhile balancing effectiveness and efficiency. This SpringerBrief targets data mining and machine learning researchers and practitionersworking in these related fields. Advanced level students studying computer scienceand electrical engineering will also find this brief useful as a study guide.