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Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms
Nazar Elfadil
College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman
Received:October 28, 2003Accepted:January 5, 2005Published:November 20, 2005
Keywords:machine learning, auto extraction, neural network, expert system, knowledge acquisition
Abstract
Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.
Cite this article as:N. Elfadil, “Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.6, pp. 693-697, 2005.Data files:
