Mining User Behavioral Rules from Smartphone Data through Association Analysis

TL;DR

This paper presents a method for mining concise, non-redundant behavioral association rules from smartphone data, improving the efficiency of understanding user habits through association analysis.

Contribution

It introduces a novel approach to identify and eliminate redundancy in association rules derived from mobile phone data, resulting in more effective behavioral rule extraction.

Findings

Effective reduction of redundant rules

Concise behavioral rule sets for individual users

Validated approach on real mobile datasets

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.