  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.
