Mining Peculiarity Groups in Day-by-Day Behavioral Datasets

Behavior mining is one of the most important issues in data mining. The growing interest in the study of behavior mining has been credited to the availability of a large amount of individual behavioral data. Some objects containing common behavioral patterns in the dataset are dramatically different from other individual objects and show their peculiarities. It is very important for behavior analysis to mine these peculiar objects' groups as this has great potential in practice. However, to the best of our knowledge, it has not been explored before. In this paper, we identify this interesting and practical problem of behavior mining: mining peculiarity groups and defining a measurement of the degree of peculiarity. As the first attempt to tackle the problem, we present a set-value-oriented day-by-day behavioral data expression mode considering that daily behaviors with respect to an object should be recorded as a set of behaviors, and devise a peculiarity group mining algorithm in view of the set-value-oriented data expression which cannot be very well handled by existing methods. Furthermore, we show that our method is practical and efficient using real datasets.