Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Learning Rules from Highly Unbalanced Data Sets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Interactive workflow mining: requirements, concepts and implementation
Data & Knowledge Engineering - Special issue: Business process management
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
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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.