# Mining User Behavioral Rules from Smartphone Data through Association Analysis

arXiv: https://arxiv.org/abs/1804.01379 · HTML: https://mlsanity.com/paper/1804.01379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [WorkR: Occupation Inference for Intelligent Task Assistance](https://mlsanity.com/paper/2407.18518/) (2024)

## Abstract (arXiv)

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.