Fuzzy association rule mining approaches for enhancing prediction performance

Sowan, Bilal, Dahal, Keshav, Hossain, Alamgir, Zhang, Li and Spencer, Linda (2013) Fuzzy association rule mining approaches for enhancing prediction performance. Expert Systems with Applications, 40 (17). pp. 6928-6937. ISSN 0957-4174

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Official URL: http://dx.doi.org/10.1016/j.eswa.2013.06.025


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.

Item Type: Article
Uncontrolled Keywords: Apriori algorithms, data mining, fuzzy C-Mean, knowledge discovery, prediction, fuzzy association rules
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 25 Jun 2013 09:50
Last Modified: 13 Oct 2019 00:36
URI: http://nrl.northumbria.ac.uk/id/eprint/13105

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