Title: An unsupervised neural network approach to predictive data mining

Authors: S.M. Monzurur Rahman, Xinghuo Yu, F.A. Siddiky

Addresses: School of Computer Science and Engineering, United International University, Road 8/A Dhanmondi, Dhaka-1209, Bangladesh. ' School of Electrical and Computer Engineering, RMIT University, GPO Box 2476, Melbourne VIC 3001, Australia. ' School of Computer Science and Engineering, United International University, Road 8/A Dhanmondi, Dhaka-1209, Bangladesh

Abstract: Rule mining is one of the popular data mining (DM) methods since rules provide concise statements of potentially important information that is easily understood by end users and are also useful patterns for predictive data mining (PDM). This paper proposes rule mining methods using an unsupervised neural network approach. Two methods are adopted based on the way of unsupervised neural networks are applied in rule mining models. In the first method, the unsupervised neural network is used for clustering, which provides class information to the rule mining process. In the second method, automated rule mining takes the place of trained neurons as it grows in a hierarchical structure of unsupervised neural network.

Keywords: classification rules; predictive data mining; PDM; rule mining; self-organising neural networks; unsupervised neural networks.

DOI: 10.1504/IJDMMM.2011.038809

International Journal of Data Mining, Modelling and Management, 2011 Vol.3 No.1, pp.1 - 17

Published online: 03 Mar 2011 *

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