Activity Mining: Challenges and Prospects
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.
KeywordsActivity data activity mining impact-targeted mining unbalanced data
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- 1.Cao, L., Zhang, C.: Domain-driven data mining: a practical methodology. Int. J. of Data Warehousing and Mining (2006)Google Scholar
- 2.Centrelink. Integrated activity management developer guide (1999)Google Scholar
- 3.Centrelink. Centrelink annual report (2004-2005)Google Scholar
- 4.Guralnik, V., Srivastava, J.: Event Detection from Time Series Data. In: KDD 1999, pp. 33–42 (1999)Google Scholar
- 6.Han, J., Pei, J., Yan, X.: Sequential Pattern Mining by Pattern-Growth: Principles and Extensions. In: Recent Advances in Data Mining and Granular Computing, Springer, Heidelberg (2005)Google Scholar
- 7.Mena, J.: Investigative Data Mining for Security and Criminal Detection, 1st edn. Butterworth-Heinemann (2003)Google Scholar
- 8.National Research Council, Making the Nation Safer: The Role of Science and Technology in Countering Terrorism, Nat’l Academy Press (2002)Google Scholar
- 10.Potts, W.: Survival Data Mining: Modeling Customer Event Histories (2006)Google Scholar
- 11.Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE TKDE 8(6), 970–974 (1996)Google Scholar
- 12.Skop, M.: Survival analysis and event history analysis. © Michal Škop (2005)Google Scholar
- 14.Williams, G., et al.: Temporal Event Mining of Linked Medical Claims Data. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, Springer, Heidelberg (2003)Google Scholar
- 15.Zhang, J., Bloedorn, E., Rosen, L., Venese, D.: Learning rules from highly unbalanced data sets. In: 2004 ICDM Proceedings, pp. 571–574 (2004)Google Scholar