{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:45:43Z","timestamp":1760708743381},"reference-count":31,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:p> The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. In order to bypass this hamper, an efficient selection of rules has to be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures gave rise to a new problem, namely the heterogeneity of the evaluation results and this created confusion to the decision. In this respect, we propose a novel approach to discover interesting association rules without favoring or excluding any measure by adopting the notion of dominance between association rules. Our approach bypasses the problem of measure heterogeneity and unveils a compromise between their evaluations. Interestingly enough, the proposed approach also avoids another non-trivial problem which is the threshold value specification. Extensive carried out experiments on benchmark datasets show the benefits of the introduced approach. <\/jats:p>","DOI":"10.1142\/s0218213014600112","type":"journal-article","created":{"date-parts":[[2014,8,26]],"date-time":"2014-08-26T23:27:06Z","timestamp":1409095626000},"page":"1460011","source":"Crossref","is-referenced-by-count":21,"title":["Mining Undominated Association Rules Through Interestingness Measures"],"prefix":"10.1142","volume":"23","author":[{"given":"Slim","family":"Bouker","sequence":"first","affiliation":[{"name":"Clermont Universit\u00e9, Universit\u00e9 Blaise Pascal, LIMOS, BP 10448, F-63000 Clermont-Ferrand, France"},{"name":"CNRS, UMR 6158, LIMOS, F-63173 Aubi\u00e8re, France"},{"name":"University of Tunis El Manar, Faculty of Sciences of Tunis, LIPAH, 2092, Tunis, Tunisia"}]},{"given":"Rabie","family":"Saidi","sequence":"additional","affiliation":[{"name":"European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, United Kingdom"}]},{"given":"Sadok","family":"Ben Yahia","sequence":"additional","affiliation":[{"name":"University of Tunis El Manar, Faculty of Sciences of Tunis, LIPAH, 2092, Tunis, Tunisia"},{"name":"T\u00e9l\u00e9com SudParis, DSI, UMR CNRS Samovar, 91011 Evry Cedex, France"}]},{"given":"Engelbert","family":"Mephu Nguifo","sequence":"additional","affiliation":[{"name":"Clermont Universit\u00e9, Universit\u00e9 Blaise Pascal, LIMOS, BP 10448, F-63000 Clermont-Ferrand, France"},{"name":"CNRS, UMR 6158, LIMOS, F-63173 Aubi\u00e8re, France"}]}],"member":"219","published-online":{"date-parts":[[2014,8,26]]},"reference":[{"key":"p_1","first-page":"207","author":"Agrawal R.","year":"1993","journal-title":"USA"},{"key":"p_2","first-page":"41","author":"Manilla H.","year":"1997","journal-title":"Greece"},{"key":"p_3","first-page":"401","author":"Klemettinen M.","year":"1994","journal-title":"Maryland, USA"},{"issue":"4","key":"p_5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.3233\/IDA-2003-7406","volume":"7","author":"Hilderman R.","year":"2003","journal-title":"Intelligent Data Analysis"},{"key":"p_6","first-page":"32","author":"Tan P. 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