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Signed Hellinger measure for directional association
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 Title & Authors
Signed Hellinger measure for directional association
Park, Hee Chang;
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 Abstract
By Wikipedia, data mining is the process of discovering patterns in a big data set involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. and database systems. Association rule is a method for discovering interesting relations between items in large transactions by interestingness measures. Association rule interestingness measures play a major role within a knowledge discovery process in databases, and have been developed by many researchers. Among them, the Hellinger measure is a good association threshold considering the information content and the generality of a rule. But it has the drawback that it can not determine the direction of the association. In this paper we proposed a signed Hellinger measure to be able to interpret operationally, and we checked three conditions of association threshold. Furthermore, we investigated some aspects through a few examples. The results showed that the signed Hellinger measure was better than the Hellinger measure because the signed one was able to estimate the right direction of association.
 Keywords
Association rule;Hellinger divergence;Hellinger measure;interestingness measure;signed Hellinger measure;
 Language
Korean
 Cited by
 References
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