ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS

Longbing Cao (), Yanchang Zhao, Chengqi Zhang and Huaifeng Zhang
Additional contact information
Longbing Cao: Faculty of Information Technology, University of Technology, Sydney, P.O. BOX 123, Broadway, NSW 2007, Australia
Yanchang Zhao: Faculty of Information Technology, University of Technology, Sydney, P.O. BOX 123, Broadway, NSW 2007, Australia
Chengqi Zhang: Faculty of Information Technology, University of Technology, Sydney, P.O. BOX 123, Broadway, NSW 2007, Australia
Huaifeng Zhang: Faculty of Information Technology, University of Technology, Sydney, P.O. BOX 123, Broadway, NSW 2007, Australia

International Journal of Information Technology & Decision Making (IJITDM), 2008, vol. 07, issue 02, 259-273

Abstract: Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading to business advantage. For instance, a series of terrorist activities may trigger a disaster to society, and large amounts of fraudulent activities in social security programs may result in huge government customer debt. Uncovering these activities or activity sequences can greatly evidence and/or enhance corresponding actions in business decisions. However, 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 based on case-study experience in the area of social security. Activity mining aims to discover high impact activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can be used to prevent disastrous events or improve business decision making and processes. We illustrate the above issues and prospects in mining governmental customer contacts data to recover customer debt.

Keywords: Activity mining; impact mining; impact modeling; imbalanced data (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622008002934
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:07:y:2008:i:02:n:s0219622008002934

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622008002934

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().