Discovery of Multiple-Level Association Rules from Large Databases

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Discovery of Multiple-Level Association Rules from Large Databases (1995)

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by Jiawei Han , Yongjian Fu

Venue:	In Proc. 1995 Int. Conf. Very Large Data Bases
Citations:	462 - 34 self

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@INPROCEEDINGS{Han95discoveryof,
    author = {Jiawei Han and Yongjian Fu},
    title = {Discovery of Multiple-Level Association Rules from Large Databases},
    booktitle = {In Proc. 1995 Int. Conf. Very Large Data Bases},
    year = {1995},
    pages = {420--431}
}

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Abstract

Previous studies on mining association rules find rules at single concept level, however, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. In this study, a top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques. A group of variant algorithms are proposed based on the ways of sharing intermediate results, with the relative performance tested on different kinds of data. Relaxation of the rule conditions for finding "level-crossing" association rules is also discussed in the paper.

Keyphrases

multiple-level association rule large database association rule level-crossing association rule multiple concept level previous study concrete knowledge multiplelevel association rule top-down progressive deepening method relative performance variant algorithm association rule mining technique single concept level intermediate result rule condition different kind large transaction database

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