@inproceedings{28246625f5804025b176d2d5139407bf,
title = "A general measure of rule interestingness",
abstract = "The paper presents a new general measure of rule interestingness. Many known measures such as chi-square, gini gain or entropy gain can be obtained from this measure by setting some numerical parameters, including the amount of trust we have in the estimation of the probability distribution of the data. Moreover, we show that there is a continuum of measures having chi-square, Gini gain and entropy gain as boundary cases. Therefore our measure generalizes both conditional and unconditional classical measures of interestingness. Properties and experimental evaluation of the new measure are also presented.",
keywords = "Cziszar divergence, Distribution, Interestingness measure, Kullback-Leibler divergence, Rule",
author = "Szymon Jaroszewicz and Simovici, \{Dan A.\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001 ; Conference date: 03-09-2001 Through 05-09-2001",
year = "2001",
doi = "10.1007/3-540-44794-6\_21",
language = "English",
isbn = "9783540425342",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "253--265",
editor = "\{De Raedt\}, Luc and Arno Siebes",
booktitle = "Principles of Data Mining and Knowledge Discovery - 5th European Conference, PKDD 2001, Proceedings",
}