POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES

摘要：

The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures dont reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesnt reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM minimizes the users effort during the post-processing process.

关键词： Association rules Post-processing Clustering and objective measures

作者: Veronica Oliveira de Carvalho Fabiano Fernandes dos Santos Solange Oliveira Rezende

作者单位: Instituto de Geocièncias e Cièncias Exatas, UNESP - Univ Estadual Paulista, Rio Claro, Brazil Instituto de Ciěncias Matem áticas e de Computacāo, USP - Universidade de Sāo Paulo, Sāo Carlos, Bra

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 1954-1963

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）