{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T06:29:25Z","timestamp":1771482565533,"version":"3.50.1"},"reference-count":55,"publisher":"Maximum Academic Press","issue":"1","license":[{"start":{"date-parts":[[2007,3,1]],"date-time":"2007-03-01T00:00:00Z","timestamp":1172707200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2007,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.<\/jats:p>","DOI":"10.1017\/s0269888907001026","type":"journal-article","created":{"date-parts":[[2007,5,25]],"date-time":"2007-05-25T09:12:35Z","timestamp":1180084355000},"page":"37-65","source":"Crossref","is-referenced-by-count":217,"title":["A review of associative classification mining"],"prefix":"10.48130","volume":"22","author":[{"given":"FADI","family":"THABTAH","sequence":"first","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2007,3,1]]},"reference":[{"key":"S0269888907001026_ref036","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"S0269888907001026_ref034","volume-title":"Proceedings of the European Conference on Machine Learning.","author":"Quinlan","year":"1993"},{"key":"S0269888907001026_ref018","volume-title":"Proceedings of the International Conference on very Large Data Bases, New York, NY","author":"Gehrke","year":"1998"},{"key":"S0269888907001026_ref020","volume-title":"Proceedings of the 16th Australasian Database Conference, Newcastle, Australia","author":"Hu","year":"2005"},{"key":"S0269888907001026_ref022","volume-title":"Proceedings of the International Conference on Data Mining (ICDM\u201901), San Jose, CA","author":"Li","year":"2001"},{"key":"S0269888907001026_ref016","doi-asserted-by":"publisher","DOI":"10.1145\/360402.360423"},{"key":"S0269888907001026_ref015","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Fayyad","year":"1998"},{"key":"S0269888907001026_ref019","volume-title":"Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.","author":"Han","year":"2000"},{"key":"S0269888907001026_ref013","volume-title":"Proceedings of the 2nd Imitational Conference on Discovery Science.","author":"Dong","year":"1999"},{"key":"S0269888907001026_ref008","unstructured":"Boutell M. , Shen X. , Luo J. & Brown C. 2003 Multi-label semantic scene classification. 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