English, Article edition: ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS LONGBING CAO; YANCHANG ZHAO; CHENGQI ZHANG; ...

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Bookmark: http://trove.nla.gov.au/version/36589
Physical Description
  • article
Language
  • English

Edition details

Title
  • ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS
Author
  • LONGBING CAO
  • YANCHANG ZHAO
  • CHENGQI ZHANG
  • HUAIFENG ZHANG
Physical Description
  • article
Notes
  • 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.
  • Activity mining, impact mining, impact modeling, imbalanced data
  • RePEc:wsi:ijitdm:v:07:y:2008:i:02:p:259-273
Language
  • English
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