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.
This work is sponsored by Australian Research Council Discovery Grant (DP0667060), China Overseas Outstanding Talent Research Program of Chinese Academy of Sciences (06S3011S01), and UTS ECRG and Chancellor grants.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cao, L., Zhang, C.: Domain-driven data mining: a practical methodology. Int. J. of Data Warehousing and Mining (2006)
Centrelink. Integrated activity management developer guide (1999)
Centrelink. Centrelink annual report (2004-2005)
Guralnik, V., Srivastava, J.: Event Detection from Time Series Data. In: KDD 1999, pp. 33–42 (1999)
Hammori, M., Herbst, J., Kleiner, N.: Interactive workflow mining—requirements, concepts and implementation. Data & Knowledge Engineering 56, 41–63 (2006)
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)
Mena, J.: Investigative Data Mining for Security and Criminal Detection, 1st edn. Butterworth-Heinemann (2003)
National Research Council, Making the Nation Safer: The Role of Science and Technology in Countering Terrorism, Nat’l Academy Press (2002)
Pazzani, M.: A Computational Theory of Learning Causal Relationships. Cognitive Science 15, 401–424 (1991)
Potts, W.: Survival Data Mining: Modeling Customer Event Histories (2006)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE TKDE 8(6), 970–974 (1996)
Skop, M.: Survival analysis and event history analysis. © Michal Škop (2005)
Van der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining: a research agenda. Computers in Industry 53, 231–244 (2004)
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)
Zhang, J., Bloedorn, E., Rosen, L., Venese, D.: Learning rules from highly unbalanced data sets. In: 2004 ICDM Proceedings, pp. 571–574 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, L. (2006). Activity Mining: Challenges and Prospects. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_65
Download citation
DOI: https://doi.org/10.1007/11811305_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)