Inducing Load Balancing and Efficient Data Distribution Prior to Association Rule Discovery in a Parallel Environment

1999 ◽  
pp. 1460-1463 ◽  
Author(s):  
Anna M. Manning ◽  
John A. Keane
Keyword(s):  
Load Balancing ◽  
Association Rule ◽  
Data Distribution ◽  
Rule Discovery ◽  
Association Rule Discovery ◽  
Efficient Data ◽  
Parallel Environment

scholarly journals Semantics-Based Classification of Rule Interestingness Measures

2009 ◽  
pp. 56-79 ◽  
Author(s):  
Julien Blanchard ◽  
Fabrice Guillet ◽  
Pascale Kuntz
Keyword(s):  
Association Rule ◽  
Rule Discovery ◽  
Interestingness Measures ◽  
Association Rule Discovery ◽  
The Subject ◽  
Rule Interestingness

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index.

scholarly journals Association Rule Discovery with Unbalanced Class Distributions

2003 ◽  
pp. 221-232 ◽  
Author(s):  
Lifang Gu ◽  
Jiuyong Li ◽  
Hongxing He ◽  
Graham Williams ◽  
Simon Hawkins ◽  
...  
Keyword(s):  
Association Rule ◽  
Rule Discovery ◽  
Association Rule Discovery
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