Rule mining and missing-Value prediction in the presence of data ambiguities

Kasun Wickramaratna, Miroslav Kubat, Kamal Premaratne, Thanuka Wickramarathne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The success of knowledge discovery in real-world domains often depends on our ability to handle data imperfections. Here we study this problem in the framework of association mining, seeking to identify frequent itemsets in transactional databases where the presence of some items in a given transaction is unknown. We want to use the frequent itemsets to predict "missing items": based on the partial contents of a shopping cart, predict what else will be added. We describe a technique that addresses this task, and report experiments illustrating its behavior.

Original languageEnglish
Title of host publicationProceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Pages361-366
Number of pages6
StatePublished - Nov 4 2009
Event22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22 - Sanibel Island, FL, United States
Duration: Mar 19 2009Mar 21 2009

Other

Other22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
CountryUnited States
CitySanibel Island, FL
Period3/19/093/21/09

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

Cite this

Wickramaratna, K., Kubat, M., Premaratne, K., & Wickramarathne, T. (2009). Rule mining and missing-Value prediction in the presence of data ambiguities. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22 (pp. 361-366)