Data mining approach using machine-oriented modeling: finding association rules using canonical names

Eric Louie; Tsau Young Lin

doi:10.1117/12.381727

6 April 2000 Data mining approach using machine-oriented modeling: finding association rules using canonical names

Eric Louie, Tsau Young Lin

Proceedings Volume 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II; (2000) https://doi.org/10.1117/12.381727
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

An attribute value, in a relational model, is a meaningful label of a collection of objects; the collection is referred to as a granule of the universe of discourse. The granule itself can be regarded a label of the collection (granule); it will be referred to as the canonical name of the granule. A relational model using these canonical names themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. For moderate size databases, finding association rules, decision rules, and etc., are reduced to easy computation of set theoretical operations of these collections. In this paper, a very fast computing algorithm is presented.

Citation Download Citation

Eric Louie and Tsau Young Lin "Data mining approach using machine-oriented modeling: finding association rules using canonical names", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); https://doi.org/10.1117/12.381727

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