Abstract—The rise of smartphones can be connected to the large number of applications, or apps, which can be installed and run by users on smartphones. It becomes important for researchers, smartphone designers, and application developers to know how users use apps on their smartphones. The aim of this paper is to present our work where data mining is applied to smartphone app usage log data with a focus on data preparation. The methods include association rule mining and sequential pattern mining. The results are the discovered rules and patterns that reflect users’ real-life app-using behaviors; they can lead to improved user interfaces, and they can also be used to reconstruct the context in which a user used his or her smartphone. We demonstrate an application of data mining that can have an impact on the smartphone industry, and we also demonstrate a data-driven approach that can help us know more about how users use apps on their smartphones.

Index Terms—Association rule mining, sequential pattern mining, smartphone app usage log.

W.-R. Tseng was with the Department of Computer Science, National Chengchi University, Taipei, Taiwan (e-mail: 100753016@nccu.edu.tw).
K.-W. Hsu is with the Department of Computer Science, National Chengchi University, Taipei, Taiwan (Corresponding author, e-mail: hsu@cs.nccu.edu.tw).

 

Cite:Woan-Rou Tseng and Kuo-Wei Hsu, "Smartphone App Usage Log Mining," International Journal of Computer and Electrical Engineering vol. 6, no.2, pp. 151-156, 2014.