The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contex- tual information and mobile phone usage records through the device logs. This paper formulates the problem of mining behavioral association rules of individual mobile phone users utilizing their smartphone data. Association rule learning is the most popular technique to discover rules utilizing large datasets. However, it is well-known that a large proportion of association rules generated are redundant. This redundant production makes not only the rule-set unnecessarily large but also makes the de- cision making process more complex and ine ective. In this paper, we propose an approach that e ectively identi es the redundancy in associ- ations and extracts a concise set of behavioral association rules that are non-redundant. The e ectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.
@article{DBLP:journals/corr/abs-1804-01379,
archiveprefix = {arXiv},
author = {Iqbal H. Sarker and
Flora D. Salim},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-01379.bib},
eprint = {1804.01379},
journal = {CoRR},
timestamp = {Wed, 24 Oct 2018 01:00:00 +0200},
title = {Mining User Behavioral Rules from Smartphone Data through Association
Analysis},
url = {http://arxiv.org/abs/1804.01379},
volume = {abs/1804.01379},
year = {2018}
}
© 2021 Flora Salim - CRUISE Research Group.