NDLI: Using association rules without understanding their underlying causality reduces their decision value

Content Provider	IEEE Xplore Digital Library
Author	Mazlack, L.J.
Copyright Year	2004
Abstract	Association rules are a data mining cornerstone. Association rules greatest impact is in helping to make decisions. One association rule quality measure is a rule's decision value. Association rules are often constructed using simplifying assumptions that lead to naive results and consequently naive decisions. Perhaps the greatest impact on decision value quality comes from treating association rules as causal statements without understanding whether there is, in fact, underlying causality. Complete knowledge of all possible factors might lead to a crisp description of whether an effect will occur. However, it is unlikely that all possible factors can be known. Commonsense world understanding accepts imprecision, uncertainty and imperfect knowledge. People recognize that a complex collection of elements can cause a particular effect. The events in the complex may not be known; or, what constraints and laws the complex is subject to. Sometimes, the details underlying an event can be known to a fine level of detail, sometimes not. Usually, commonsense reasoning is more successful in reasoning about a few large-grain sized events than many fine-grained events. A satisficing solution would be to develop large-grained solutions and only use the flner- grain when the impreciseness of the large-grain is unsatisfactory.
Starting Page	312
Ending Page	319
File Size	279227
Page Count	8
File Format	PDF
ISBN	0780388232
DOI	10.1109/ICMLA.2004.1383529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Laboratories Humans Glass Modems Association rules Data mining Automobiles Computational intelligence Accidents
Content Type	Text
Resource Type	Article

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