Discovering Strong Meta Association Rules Using Bees Swarm Optimization
作者： Youcef Djenouri , Asma Belhadi , Philippe Fournier-Viger , Jerry Chun-Wei Lin
DOI: 10.1007/978-3-030-04503-6_21
关键词: Data mining 、 Swarm behaviour 、 Quality (business) 、 Association rule learning 、 Computer science 、 Set (abstract data type)
摘要: For several applications, association rule mining produces an extremely large number of rules. Analyzing a rules can be very time-consuming for users. Therefore, eliminating irrelevant is necessary. This paper addresses this problem by proposing efficient approach based on the concept meta The algorithm first discovers dependencies between called Then, these are used to eliminate that replaced more general rule. Because set meta-rules large, bee swarm optimization applied quickly extract strongest meta-rules. has been synthetic dataset and compared with state-of-the-art algorithm. Results promising in terms found their quality.
springer.com
sci-hub.st 参考文章(29)
Leandro A. F. Fernandes, Ana Cristina Bicharra García, Association Rule Visualization and Pruning through Response-Style Data Organization and Clustering ibero-american conference on artificial intelligence. pp. 71- 80 ,(2012) , 10.1007/978-3-642-34654-5_8
Zoran Mitrovic, Zita Bošnjak, Discovering Interesting Association Rules in the Web Log Usage Data Interdisciplinary Journal of Information, Knowledge, and Management. ,vol. 5, pp. 191- 207 ,(2010) , 10.28945/1159
Bing Liu, Wynne Hsu, Ke Wang, Shu Chen, Visually Aided Exploration of Interesting Association Rules pacific asia conference on knowledge discovery and data mining. pp. 380- 389 ,(1999) , 10.1007/3-540-48912-6_52
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier, Lotfi Lakhal, Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis Lecture Notes in Computer Science. pp. 335- 350 ,(2001) , 10.1007/3-540-45422-5_24
Gunjan Mansingh, Kweku-Muata Osei-Bryson, Han Reichgelt, Using ontologies to facilitate post-processing of association rules by domain experts Information Sciences. ,vol. 181, pp. 419- 434 ,(2011) , 10.1016/J.INS.2010.09.027
Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo, Finding interesting rules from large sets of discovered association rules conference on information and knowledge management. pp. 401- 407 ,(1994) , 10.1145/191246.191314
Abdelaziz Berrado, George C. Runger, Using metarules to organize and group discovered association rules Data Mining and Knowledge Discovery. ,vol. 14, pp. 409- 431 ,(2007) , 10.1007/S10618-006-0062-6
Bing Liu, Wynne Hsu, Yiming Ma, Pruning and summarizing the discovered associations knowledge discovery and data mining. pp. 125- 134 ,(1999) , 10.1145/312129.312216
Youcef Djenouri, Youcef Gheraibia, Malika Mehdi, Ahcene Bendjoudi, Nadia Nouali-Taboudjemat, An efficient measure for evaluating association rules soft computing and pattern recognition. pp. 406- 410 ,(2014) , 10.1109/SOCPAR.2014.7008041
Toshihiko Watanabe, An improvement of fuzzy association rules mining algorithm based on redundacy of rules 2010 2nd International Symposium on Aware Computing. pp. 68- 73 ,(2010) , 10.1109/ISAC.2010.5670457