Classification of association rules based on K-means algorithm
作者： Azzeddine Dahbi , Mohamed Mouhir , Youssef Balouki , Taoufiq Gadi
DOI: 10.1109/CIST.2016.7805061
关键词: Machine learning 、 Selection (genetic algorithm) 、 k-means clustering 、 Artificial intelligence 、 Thesaurus (information retrieval) 、 Association rule learning 、 Correlation 、 Cluster analysis 、 Computer science 、 Statistical classification 、 Data mining 、 Obstacle
摘要: Association rule mining is one of the most relevant techniques in data mining, aiming to extract correlation among sets items or products transactional databases. The huge number association rules extracted represents main obstacle that a decision maker faces. Hence, many interestingness measures have been proposed evaluate rules. However, abundance these caused new problem, which selection best suited users. To bypass this we propose an approach based on K-means algorithm classify and store Rules without favoring excluding any measures. experiments, performed numerous datasets, show significant performance it effectively
sci-hub.se 参考文章(18)
Z. Guellil, Lynda Zaoui, Proposition d'une Solution au Problème d'Initialisation Cas du K-means. CIIA. ,(2009)
Ramakrishnan Srikant, Rakesh Agrawal, Fast algorithms for mining association rules very large data bases. pp. 580- 592 ,(1998)
Khaled Alsabti, Sanjay Ranka, Vineet Singh, An efficient k-means clustering algorithm ,(1997)
Benoît Vaillant, Philippe Lenca, Stéphane Lallich, A Clustering of Interestingness Measures discovery science. pp. 290- 297 ,(2004) , 10.1007/978-3-540-30214-8_23
Slim Bouker, Rabie Saidi, Sadok Ben Yahia, Engelbert Mephu Nguifo, Mining Undominated Association Rules Through Interestingness Measures International Journal on Artificial Intelligence Tools. ,vol. 23, pp. 1460011- ,(2014) , 10.1142/S0218213014600112
Philippe Lenca, Patrick Meyer, Benoît Vaillant, Stéphane Lallich, On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid European Journal of Operational Research. ,vol. 184, pp. 610- 626 ,(2008) , 10.1016/J.EJOR.2006.10.059
Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava, Selecting the right interestingness measure for association patterns Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02. pp. 32- 41 ,(2002) , 10.1145/775047.775053
Mohammed J. Zaki, CHARM: An Efficient Algorithm for Closed Association Rule Mining ,(2007)
David Arthur, Sergei Vassilvitskii, k-means++: the advantages of careful seeding symposium on discrete algorithms. pp. 1027- 1035 ,(2007) , 10.5555/1283383.1283494
Longbing Cao, Dan Luo, Chengqi Zhang, Knowledge actionability: satisfying technical and business interestingness International Journal of Business Intelligence and Data Mining. ,vol. 2, pp. 496- 514 ,(2007) , 10.1504/IJBIDM.2007.016385