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A dynamic semisupervised feedforward neural network clustering

Published online by Cambridge University Press:  03 May 2016

Roya Asadi ,
Sameem Abdul Kareem ,
Shokoofeh Asadi  and
Mitra Asadi
Roya Asadi*
Affiliation:
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
Sameem Abdul Kareem
Affiliation:
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
Shokoofeh Asadi
Affiliation:
Department of Agricultural Management Engineering, Faculty of Ebne-Sina, University of Science and Research Branch, Tehran, Iran
Mitra Asadi
Affiliation:
Department of Research, Iranian Blood Transfusion Organization, Tehran, Iran
*
Reprint requests to: Roya Asadi, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 60503, Selangor, Malaysia. E-mail: royaasadi@siswa.um.edu.my
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Abstract

An efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

Keywords

Artificial Neural NetworkFeedforward Neural NetworkNonrandom WeightOnline Dynamic LearningSemisupervised ClusteringSupervised and Unsupervised Learning
Type
Regular Articles
Information
AI EDAM , Volume 31 , Issue 1 , February 2017 , pp. 30 - 54
Copyright
Copyright © Cambridge University Press 2016 

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