Fuzzy association rule mining approaches for enhancing prediction performance

作     者：Sowan, Bilal Dahal, Keshav Hossain, M. A. Zhang, Li Spencer, Linda 

作者机构：Univ Bradford Sch Comp Informat & Media Bradford BD7 1DP W Yorkshire England Northumbria Univ Computat Intelligence Grp Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2013年第40卷第17期

页      面：6928-6937页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Apriori algorithms Data mining Fuzzy C-Mean Knowledge discovery Prediction Fuzzy association rules 

摘      要：This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM-MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM-MSapriori model predicted the future values effectively and outperformed the FCM-Apriori model and other models reported in the literature. (C) 2013 Elsevier Ltd. All rights reserved.