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An efficient semisupervised feedforward neural network clustering

Published online by Cambridge University Press:  02 December 2014

Roya Asadi ,
Mitra Asadi  and
Sameem Abdul Kareem
Roya Asadi*
Affiliation:
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
Mitra Asadi
Affiliation:
Department of Research, Iranian Blood Transfusion Organization, Tehran, Iran
Sameem Abdul Kareem
Affiliation:
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
*
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

We developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superior F measure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.

Keywords

Artificial Neural NetworkFeedforward Neural NetworkNonrandom WeightSemiclusteringSupervised and Unsupervised Learning
Type
Regular Articles
Information
AI EDAM , Volume 30 , Issue 1 , February 2016 , pp. 1 - 15
Copyright
Copyright © Cambridge University Press 2014 

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