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Ontological Frequent Patterns Mining by potential use of Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 36 - Number 10 |
| Year of Publication: 2011 |
| Authors: Amit Bhagat, Sanjay Sharma, K. R. Pardasani |
10.5120/4529-6465
|
Amit Bhagat, Sanjay Sharma, K. R. Pardasani . Ontological Frequent Patterns Mining by potential use of Neural Network. International Journal of Computer Applications. 36, 10 ( December 2011), 44-53. DOI=10.5120/4529-6465
Abstract
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 multiple-level 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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