Mining User Behavioral Rules from Smartphone Data through Association Analysis

Sarker, I and Salim, F 2018, 'Mining User Behavioral Rules from Smartphone Data through Association Analysis', in Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 10937., Melbourne, Australia, 3-6 June 2018, pp. 450-461.


Document type: Conference Paper
Collection: Conference Papers

Title Mining User Behavioral Rules from Smartphone Data through Association Analysis
Author(s) Sarker, I
Salim, F
Year 2018
Conference name Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018.
Conference location Melbourne, Australia
Conference dates 3-6 June 2018
Proceedings title Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 10937.
Publisher Springer International Publishing AG, part of Springer Nature 2018
Place of publication Melbourne, Australia
Start page 450
End page 461
Total pages 12
Abstract 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.
Subjects Pattern Recognition and Data Mining
Ubiquitous Computing
Keyword(s) Mobile data mining
Association rule mining
Non-redundancy
Contexts
User behavior modeling
Copyright notice © Springer International Publishing AG, part of Springer Nature 2018
ISBN 9783319930343
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