scholarly journals Measures of rule interestingness in various perspectives of confirmation

2016 ◽  
Vol 346-347 ◽  
pp. 216-235 ◽  
Author(s):  
Salvatore Greco ◽  
Roman Słowiński ◽  
Izabela Szczęch
Keyword(s):  
2010 ◽  
Vol 34-35 ◽  
pp. 1961-1965
Author(s):  
You Qu Chang ◽  
Guo Ping Hou ◽  
Huai Yong Deng

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.


Author(s):  
Julien Blanchard ◽  
Fabrice Guillet ◽  
Pascale Kuntz

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.


Author(s):  
Miho Ohsaki ◽  
Shinya Kitaguchi ◽  
Hideto Yokoi ◽  
Takahira Yamaguchi

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