{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:40:57Z","timestamp":1783478457513,"version":"3.55.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ONR","award":["N00014-16-1-2918"],"award-info":[{"award-number":["N00014-16-1-2918"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>\n                    Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form \u201cIf\n                    <jats:italic toggle=\"yes\">X<\/jats:italic>\n                    is true at time\n                    <jats:italic toggle=\"yes\">t<\/jats:italic>\n                    , then\n                    <jats:italic toggle=\"yes\">Y<\/jats:italic>\n                    will likely be true at time (\n                    <jats:italic toggle=\"yes\">t<\/jats:italic>\n                    +\u03c4).\u201d As with ARs, solving the TAR mining problem can generate huge numbers of rules. We show that methods to summarize ARs cannot work directly with TARs, and we develop two notions\u2014\n                    <jats:italic toggle=\"yes\">strong<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">weak<\/jats:italic>\n                    summaries\u2014to summarize a set of TARs. We show that the problems of finding strong\/weak summaries are NP-hard, and we provide polynomial-time approximation algorithms. We show experimentally that the coverage provided by our summarization methods is very high. Both technical measures based on coverage and human experiments on six World Bank datasets using 100 subjects from Mechanical Turk and a separate experiment with terrorism experts on a terrorism dataset show that while both summarization methods perform well, weak summaries are preferred, despite their taking more time to compute than strong summaries.\n                  <\/jats:p>","DOI":"10.1145\/3419107","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T12:06:42Z","timestamp":1609675602000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["STAR"],"prefix":"10.1145","volume":"2","author":[{"given":"Cristian","family":"Molinaro","sequence":"first","affiliation":[{"name":"University of Calabria, Rende (CS), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Pulice","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anja","family":"Subasic","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail","family":"Bartolome","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. S.","family":"Subrahmanian","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Foto N. Afrati Aristides Gionis and Heikki Mannila. 2004. Approximating a collection of frequent sets. In KDD. 12--19.","DOI":"10.1145\/1014052.1014057"},{"key":"e_1_2_1_2_1","volume-title":"Yu","author":"Aggarwal Charu C.","year":"1998","unstructured":"Charu C. Aggarwal and Philip S. Yu. 1998. Online generation of association rules. In ICDE. 402--411."},{"key":"e_1_2_1_3_1","volume-title":"Swami","author":"Agrawal Rakesh","year":"1993","unstructured":"Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. 1993. Mining association rules between sets of items in large databases. In SIGMOD. 207--216."},{"key":"e_1_2_1_4_1","volume-title":"Ale and Gustavo Rossi","author":"Juan","year":"2000","unstructured":"Juan M. Ale and Gustavo Rossi. 2000. An approach to discovering temporal association rules. In SAC. 294--300."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Mohamed-Bachir Belaid Christian Bessiere and Nadjib Lazaar. 2019. Constraint programming for association rules. In SDM. 127--135.","DOI":"10.1137\/1.9781611975673.15"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Abdelhamid Boudane Sa\u00efd Jabbour Lakhdar Sais and Yakoub Salhi. 2017. Enumerating non-redundant association rules using satisfiability. In PAKDD. 824--836.","DOI":"10.1007\/978-3-319-57454-7_64"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Cheng-Yue Chang Ming-Syan Chen and Chang-Hung Lee. 2002. Mining general temporal association rules for items with different exhibition periods. In ICDM. 59--66.","DOI":"10.1109\/ICDM.2002.1183886"},{"key":"e_1_2_1_8_1","unstructured":"Gautam Das King-Ip Lin Heikki Mannila Gopal Renganathan and Padhraic Smyth. 1998. Rule discovery from time series. In KDD. 16--22."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2010.03.002"},{"key":"e_1_2_1_10_1","volume-title":"Rough Sets, Fuzzy Sets and Knowledge Discovery","author":"Golan Robert","unstructured":"Robert Golan and Donald Edwards. 1994. Temporal rules discovery using datalogic\/R+ with stock market data. In Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer, 74--81."},{"key":"e_1_2_1_11_1","volume-title":"Webb","author":"H\u00e4m\u00e4l\u00e4inen Wilhelmiina","year":"2017","unstructured":"Wilhelmiina H\u00e4m\u00e4l\u00e4inen and Geoffrey I. Webb. 2017. Specious rules: An efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining. In SDM. 309--317."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335372"},{"key":"e_1_2_1_13_1","volume-title":"Peter Neumayer, Yong Ding, Till Riedel, and Michael Beigl.","author":"Han Wei","year":"2017","unstructured":"Wei Han, Julio De Melo Borges, Peter Neumayer, Yong Ding, Till Riedel, and Michael Beigl. 2017. Interestingness classification of association rules for master data. In ICDM. 237--245."},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Roberto J. Bayardo Jr. Rakesh Agrawal and Dimitrios Gunopulos. 1999. Constraint-based rule mining in large dense databases. In ICDE. 188--197.","DOI":"10.1109\/ICDE.1999.754924"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1209015"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Chang-Hung Lee Cheng-Ru Lin and Ming-Syan Chen. 2001. On mining general temporal association rules in a publication database. In ICDM. 337--344.","DOI":"10.1109\/ICDM.2001.989537"},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Brian Lent Arun N. Swami and Jennifer Widom. 1997. Clustering association rules. In ICDE. 220--231.","DOI":"10.1109\/ICDE.1997.581756"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-023X(02)00135-0"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Bing Liu Wynne Hsu and Yiming Ma. 1999. Pruning and summarizing the discovered associations. In KDD. 125--134.","DOI":"10.1145\/312129.312216"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.27"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/358108.358114"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Michael Mampaey Nikolaj Tatti and Jilles Vreeken. 2011. Tell me what i need to know: Succinctly summarizing data with itemsets. In KDD. 573--581.","DOI":"10.1145\/2020408.2020499"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009748302351"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2012.01.013"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01588971"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.07.031"},{"key":"e_1_2_1_27_1","volume-title":"Salim","author":"Sarker Iqbal H.","year":"2018","unstructured":"Iqbal H. Sarker and Flora D. Salim. 2018. Mining user behavioral rules from smartphone data through association analysis. In PAKDD. 450--461."},{"key":"e_1_2_1_28_1","volume-title":"Dickerson","author":"Subrahmanian V. S.","year":"2012","unstructured":"V. S. Subrahmanian, Aaron Mannes, Amy Sliva, Jana Shakarian, and John P. Dickerson. 2012. Computational Analysis of Terrorist Groups: Lashkar-e-Taiba. Springer."},{"key":"e_1_2_1_29_1","volume-title":"Muntz","author":"Wang Wei","year":"2001","unstructured":"Wei Wang, Jiong Yang, and Richard R. Muntz. 2001. TAR: Temporal association rules on evolving numerical attributes. In ICDE. 283--292."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.10.009"},{"key":"e_1_2_1_31_1","unstructured":"Dong Xin Jiawei Han Xifeng Yan and Hong Cheng. 2005. Mining compressed frequent-pattern sets. In VLDB. 709--720."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0446-6"}],"container-title":["ACM\/IMS Transactions on Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3419107","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3419107","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3419107","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:56:49Z","timestamp":1776347809000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3419107"}},"subtitle":["Summarizing Timed Association Rules"],"short-title":[],"issued":{"date-parts":[[2021,1,3]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3419107"],"URL":"https:\/\/doi.org\/10.1145\/3419107","relation":{},"ISSN":["2691-1922"],"issn-type":[{"value":"2691-1922","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,3]]},"assertion":[{"value":"2019-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-08-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}