{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T22:41:37Z","timestamp":1654123297608},"reference-count":28,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,10,1]]},"abstract":"<p>Updates on an operational database bring forth the challenge of keeping the frequent itemsets up-to-date without re-running the itemset mining algorithms. Studies on dynamic itemset mining, which is the solution to such an update problem, have to address some challenges as handling i) updates without re-running the base algorithm, ii) changes in the support threshold, iii) new items and iv) additions\/deletions in updates. The study in this paper is the extension of the Incremental Matrix Apriori Algorithm which proposes solutions to the first three challenges besides inheriting the advantages of the base algorithm which works without candidate generation. In the authors' current work, the authors have improved a former algorithm as to handle updates that are composed of additions and deletions. The authors have also carried out a detailed performance evaluation study on a real and two benchmark datasets.<\/p>","DOI":"10.4018\/ijdwm.2013100104","type":"journal-article","created":{"date-parts":[[2014,4,9]],"date-time":"2014-04-09T13:15:02Z","timestamp":1397049302000},"page":"62-75","source":"Crossref","is-referenced-by-count":4,"title":["DMA"],"prefix":"10.4018","volume":"9","author":[{"given":"Damla","family":"Oguz","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baris","family":"Yildiz","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey & Department of Computer Engineering, Ege University, Izmir, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Belgin","family":"Ergenc","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey  & Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"ijdwm.2013100104-0","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77623-9_21"},{"key":"ijdwm.2013100104-1","doi-asserted-by":"publisher","DOI":"10.1145\/170036.170072"},{"key":"ijdwm.2013100104-2","unstructured":"Agrawal, R., & Srikant, R. 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Ali (Eds.), Proceedings of the 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 169\u2013178). Berlin, Germany: Springer."},{"key":"ijdwm.2013100104-17","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009796218281"},{"key":"ijdwm.2013100104-18","doi-asserted-by":"crossref","unstructured":"Oguz, D., & Ergenc, B. (2012). Incremental itemset mining based on Matrix Apriori algorithm. In A. Cuzzocrea & U. Dayal (Eds.), Proceedings of the14th International Conference on Data Warehousing and Knowledge Discovery (pp. 192\u2013204). Springer-Verlag.","DOI":"10.1007\/978-3-642-32584-7_16"},{"key":"ijdwm.2013100104-19","doi-asserted-by":"publisher","DOI":"10.1145\/568271.223813"},{"key":"ijdwm.2013100104-20","unstructured":"Pav\u00f3n, J., Paulo, S., & Viana, S. (2006). Matrix Apriori: Speeding up the search for frequent patterns. In M. H. 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