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Data"],"published-print":{"date-parts":[[2014,6,2]]},"abstract":"<jats:p>\n            Self-sufficient itemsets have been proposed as an effective approach to summarizing the key associations in data. However, their computation appears highly demanding, as assessing whether an itemset is self-sufficient requires consideration of all pairwise partitions of the itemset into pairs of subsets as well as consideration of all supersets. This article presents the first published algorithm for efficiently discovering self-sufficient itemsets. This branch-and-bound algorithm deploys two powerful pruning mechanisms based on upper bounds on itemset value and statistical significance level. It demonstrates that finding top-\n            <jats:italic>k<\/jats:italic>\n            productive and nonredundant itemsets, with postprocessing to identify those that are not independently productive, can efficiently identify small sets of key associations. We present extensive evaluation of the strengths and limitations of the technique, including comparisons with alternative approaches to finding the most interesting associations.\n          <\/jats:p>","DOI":"10.1145\/2601433","type":"journal-article","created":{"date-parts":[[2014,7,7]],"date-time":"2014-07-07T14:46:44Z","timestamp":1404744404000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Efficient Discovery of the Most Interesting Associations"],"prefix":"10.1145","volume":"8","author":[{"given":"Geoffrey I.","family":"Webb","sequence":"first","affiliation":[{"name":"Monash University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jilles","family":"Vreeken","sequence":"additional","affiliation":[{"name":"University of Antwerp, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"volume-title":"Proceedings of the 1st International Conference on Computational Logic (CL\u201900)","author":"Bastide Y.","key":"e_1_2_1_2_1","unstructured":"Y. Bastide , N. Pasquier , R. Taouil , G. Stumme , and L. Lakhal . 2000. Mining minimal non-redundant association rules using frequent closed itemsets . In Proceedings of the 1st International Conference on Computational Logic (CL\u201900) . Springer-Verlag, Berlin, 972--986. Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal. 2000. Mining minimal non-redundant association rules using frequent closed itemsets. In Proceedings of the 1st International Conference on Computational Logic (CL\u201900). Springer-Verlag, Berlin, 972--986."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009895914772"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312241"},{"volume-title":"Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKKD\u201902)","author":"Calders T.","key":"e_1_2_1_5_1","unstructured":"T. Calders and B. Goethals . 2002. Mining all non-derivable frequent itemsets . In Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKKD\u201902) . Springer, Berlin, 74--85. T. Calders and B. Goethals. 2002. Mining all non-derivable frequent itemsets. In Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKKD\u201902). Springer, Berlin, 74--85."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0054-6"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0209-3"},{"volume-title":"Proceedings of the 12th International Symposium on Foundations of Intelligent Systems. 59--67","author":"Fu A. W. C.","key":"e_1_2_1_8_1","unstructured":"A. W. C. Fu , W. K. Renfrew , and J. Tang . 2000. Mining N-most interesting itemsets . In Proceedings of the 12th International Symposium on Foundations of Intelligent Systems. 59--67 . A. W. C. Fu, W. K. Renfrew, and J. Tang. 2000. Mining N-most interesting itemsets. In Proceedings of the 12th International Symposium on Foundations of Intelligent Systems. 59--67."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3120747.3120794"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1132960.1132963"},{"volume-title":"Proceedings of the 82nd Annual Transportation Research Board.","author":"Geurts K.","key":"e_1_2_1_11_1","unstructured":"K. Geurts , G. Wets , T. Brijs , and K. Vanhoof . 2003. Profiling high frequency accident locations using association rules . In Proceedings of the 82nd Annual Transportation Research Board. K. Geurts, G. Wets, T. Brijs, and K. Vanhoof. 2003. Profiling high frequency accident locations using association rules. In Proceedings of the 82nd Annual Transportation Research Board."},{"key":"e_1_2_1_12_1","volume-title":"Retrieved","author":"Goethals B.","year":"2012","unstructured":"B. Goethals . 2012 . Frequent Itemset Mining Implementations Repository . Retrieved April 26, 2014, from http:\/\/fimi.ua.ac.be\/. B. Goethals. 2012. Frequent Itemset Mining Implementations Repository. Retrieved April 26, 2014, from http:\/\/fimi.ua.ac.be\/."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0432-2"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0059-1"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557065"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-008-0102-5"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1982.12425"},{"volume-title":"Proceedings of the 10th SIAM International Conference on Data Mining (SDM\u201910)","author":"Kontonasios K.-N.","key":"e_1_2_1_19_1","unstructured":"K.-N. Kontonasios and T. De Bie . 2010. An information-theoretic approach to finding noisy tiles in binary databases . In Proceedings of the 10th SIAM International Conference on Data Mining (SDM\u201910) . 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