Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes

Micheline Kamber, Jiawei Han, Jenny Y. Chiang

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

Abstract

In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-guided mining: given a metarule containing p predicates, we compare mining on an n-dimensional (w-D) cube structure (where p < n) with mining on smaller multiple p-dimensional cubes. In addition, we propose an efficient method for precomputing the cube, which takes into account the constraints imposed by the given metarule.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
EditorsDavid Heckerman, Heikki Mannila, Daryl Pregibon, Ramasamy Uthurusamy
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Pages207-210
Number of pages4
ISBN (Electronic)1577350278, 9781577350279
StatePublished - 1997
Externally publishedYes
Event3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 - Newport Beach, United States
Duration: Aug 14 1997Aug 17 1997

Publication series

NameProceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Conference

Conference3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Country/TerritoryUnited States
CityNewport Beach
Period8/14/978/17/97

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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