Fuzzy multiple support associative classification approach for prediction

Sowan, Bilal, Dehal, Keshav and Hossain, Alamgir (2010) Fuzzy multiple support associative classification approach for prediction. In: Lecture Notes in Artificial Intelligence. Springer, London, pp. 216-223. ISBN 9783642132070

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Official URL: http://dx.doi.org/10.1007/978-3-642-13208-7_28

Abstract

The fact of building an accurate classification and prediction system remains one of the most significant challenges in knowledge discovery and data mining. In this paper, a Knowledge Discovery (KD) framework is proposed; based on the integrated fuzzy approach, more specifically Fuzzy C-Means (FCM) and the new Multiple Support Classification Association Rules (MSCAR) algorithm. MSCAR is considered as an efficient algorithm for extracting both rare and frequent rules using vertical scanning format for the database. Consequently, the adaptation of such a process sufficiently minimized the prediction error. The experimental results regarding two data sets; Abalone and road traffic, show the effectiveness of the proposed approach in building a robust prediction system. The results also demonstrate that the proposed KD framework outperforms the existing prediction systems.

Item Type: Book Section
Uncontrolled Keywords: knowledge discovery, MSapriori, Apriori, fuzzy C-Means, classification
Subjects: G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: EPrint Services
Date Deposited: 04 Aug 2011 15:12
Last Modified: 04 Aug 2017 10:01
URI: http://nrl.northumbria.ac.uk/id/eprint/1997

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