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Ontological Frequent Patterns Mining by potential use of Neural Network

Author(s): Amit Bhagat | Sanjay Sharma | K. R. Pardasani

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 36;
Issue: 10;
Start page: 44;
Date: 2011;
Original page

Keywords: Non-uniform support | Multilayer Perceptron network | Frequent item sets | Algorithms | Neural Network

Association rule mining has attracted wide attention in both research and application areas recently. The mining of multilevel association rules is one of the important branches of it. Mining association rules at multiple levels helps in finding more specific and relevant knowledge. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. In this paper, an efficient algorithm named Multi Level Feed Forward Mining 'MLFM' is proposed for efficient mining of multiplelevel association rules from large transaction databases. This algorithm uses Feed Forward Neural Networks as Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. So we have used supervised neural network in parallel for finding frequent item sets at each concept levels in only single scan of database.
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