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Finding Interesting Rules from Large Sets of Discovered Association Rules (1994)
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BibTeX
@INPROCEEDINGS{Klemettinen94findinginteresting,
author = {Mika Klemettinen and Heikki Mannila and Pirjo Ronkainen and Hannu Toivonen and A. Inkeri Verkamo},
title = {Finding Interesting Rules from Large Sets of Discovered Association Rules},
booktitle = {},
year = {1994},
pages = {401--407}
}
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Abstract
Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates. 1 Introduction Data mining (knowledge discovery in databases) is a field of increasing interest combining databases, artificial intelligence, and machine learning. The purpose of data mining is to facilitate understanding large amounts of data by discovering interesting regularities or exceptions (see e...
Keyphrases
discovered association rule large set rule template interesting rule association rule large collection visualization tool interface simple formalism machine learning interesting regularity interest combining database data mining introduction data mining efficient method understanding large amount discovered rule artificial intelligence
