Mining User Behavioral Rules from Smartphone Data Through Association Analysis

Publication Year: 2018 Publication Type : ConferenceProceeding


The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual 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 decision making process more complex and ineffective. In this paper, we propose an approach that effectively identifies the redundancy in associations and extracts a concise set of behavioral association rules that are non-redundant. The effectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.


    author = {Iqbal H. Sarker and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I},
    doi = {10.1007/978-3-319-93034-3_36},
    editor = {Dinh Q. Phung and Vincent S. Tseng and Geoffrey I. Webb and Bao Ho and Mohadeseh Ganji and Lida Rashidi},
    pages = {450--461},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Mon, 15 Jun 2020 01:00:00 +0200},
    title = {Mining User Behavioral Rules from Smartphone Data Through Association Analysis},
    url = {},
    volume = {10937},
    year = {2018}


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