Abstract/Description
An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced to only those attributes which contribute in the training process. Then after, the whole dataset is partitioned into n clusters which are finally fed into multilayer perceptrons network based on backpropagation algorithm to carry out blockwise and parallel training.
Keywords
Multilayer perceptrons, Artificial neural networks, Data mining, Backpropagation algorithms, Neural networks, Clustering algorithms, Data analysis, Psychology, System testing, Computer networks
Session Theme
Artificial Intelligence – I
Session Type
Other
Session Chair
Dr. Sajjad Haider
Start Date
15-8-2009 3:25 PM
End Date
15-8-2009 3:45 PM
Recommended Citation
Ullah, S., & Hussain, Z. (2009). Artificial Intelligence – I: A two-step approach for improving efficiency of feedforward Multilayer Perceptrons network. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2009/2009/21
Included in
Data Science Commons, Digital Communications and Networking Commons, OS and Networks Commons, Theory and Algorithms Commons
Artificial Intelligence – I: A two-step approach for improving efficiency of feedforward Multilayer Perceptrons network
An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced to only those attributes which contribute in the training process. Then after, the whole dataset is partitioned into n clusters which are finally fed into multilayer perceptrons network based on backpropagation algorithm to carry out blockwise and parallel training.
