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Knowl. Discov. Data"],"published-print":{"date-parts":[[2015,10,26]]},"abstract":"<jats:p>\n            Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use\n            <jats:italic>support<\/jats:italic>\n            (frequency) to measure the\n            <jats:italic>popularity<\/jats:italic>\n            of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the\n            <jats:italic>completeness<\/jats:italic>\n            of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern\n            <jats:italic>dominant<\/jats:italic>\n            if its occupancy value is above a user-specified threshold. Then, our task is to identify the\n            <jats:italic>qualified patterns<\/jats:italic>\n            which are both dominant and frequent. Also, we formulate the problem of\n            <jats:italic>mining top-k qualified patterns<\/jats:italic>\n            , that is, finding\n            <jats:italic>k<\/jats:italic>\n            qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.\n          <\/jats:p>","DOI":"10.1145\/2753765","type":"journal-article","created":{"date-parts":[[2015,10,13]],"date-time":"2015-10-13T22:31:00Z","timestamp":1444775460000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Occupancy-Based Frequent Pattern Mining\n            <sup>*<\/sup>"],"prefix":"10.1145","volume":"10","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"Anhui University, Anhui Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linpeng","family":"Tang","sequence":"additional","affiliation":[{"name":"Princeton University, Princeton, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"Google Research, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers, the State University of New Jersey, Newark, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,10,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281201"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/645480.655281"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775109"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/1032649.1033434"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.02.006"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/645484.656386"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.10.057"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1497577.1497580"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.4304\/jnw.9.2.252-258"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 27th Canadian Conference on Artificial Intelligence (AI","author":"Fournier-Viger Philippe","year":"2014","unstructured":"Philippe Fournier-Viger , Cheng Wei Wu , Antonio Gomariz Penalver , and Vincent S. Tseng . 2014. VMSP: Efficient vertical mining of maximal sequential patterns . In Proceedings of the 27th Canadian Conference on Artificial Intelligence (AI 2014 ). Springer International Publishing, 83--94. Philippe Fournier-Viger, Cheng Wei Wu, Antonio Gomariz Penalver, and Vincent S. Tseng. 2014. VMSP: Efficient vertical mining of maximal sequential patterns. In Proceedings of the 27th Canadian Conference on Artificial Intelligence (AI 2014). Springer International Publishing, 83--94."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014070"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 25th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc.","author":"Garofalakis Minos N.","year":"1999","unstructured":"Minos N. Garofalakis , Rajeev Rastogi , and Kyuseok Shim . 1999 . SPIRIT: Sequential pattern mining with regular expression constraints . In Proceedings of the 25th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc. , San Francisco, CA, USA. 223--234. Minos N. Garofalakis, Rajeev Rastogi, and Kyuseok Shim. 1999. SPIRIT: Sequential pattern mining with regular expression constraints. In Proceedings of the 25th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. 223--234."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30214-8_22"},{"key":"e_1_2_1_15_1","unstructured":"Anna Gorbenko. 2012. On the longest common subsequence problem. Applied Mathematical Sciences 6 116 5781--5787.  Anna Gorbenko. 2012. On the longest common subsequence problem. 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