Abstract:
Activity data accumulated in real life, e.g. in terrorist activities and
fraudulent customer contacts, presents special structural and semantic
complexities. However, it may lead to or be associated with significant business
impacts. For instance, a series of terrorist activities may trigger a disaster to the
society, large amounts of fraudulent activities in social security program may
result in huge government customer debt. Mining such data challenges the
existing KDD research in aspects such as unbalanced data distribution and
impact-targeted pattern mining. This paper investigates the characteristics and
challenges of activity data, and the methodologies and tasks of activity mining.
Activity mining aims to discover impact-targeted activity patterns in huge
volumes of unbalanced activity transactions. Activity patterns identified can
prevent disastrous events or improve business decision making and processes.
We illustrate issues and prospects in mining governmental customer contacts.
