Home / Journals American Journal of Neural Networks and Applications / Unsupervised Neural Network Clustering
Unsupervised Neural Network Clustering
Submission Deadline: Aug. 30, 2017

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Roya Asadi
Neural Network, Department of Artificial Intelligence, Faculty of Computer Science & IT, University of Malaya, Kuala Lumpur, Selangor, Malaysia
Guest Editors
  • Loo Chu Kiong
    Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia
  • Hamid Abdulla Jallb Al-Tulea
    Department of Computer System & Technology, Faculty of Computer Science & Information Technology, Kuala Lumpur, Malaysia
  • S. Raviraja
    Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia
  • Sameem Binti Abdul Kareem
    Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, Kuala Lumpur, Malaysia
  • Md. Nasir Bin Sulaiman
    Department of Computer Science, Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang, Malaysia
  • Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
  • Christo Ananth
    Department of Electrical and Computer Engineering, Francis Xavier Engineering College, Tirunelveli, India
  • Meng Ding
    U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
  • Mahmut Sinecen
    Computer Engineering Department, Adnan Menderes University, Aydın, Merkez, Turkey
  • Yi Zhang
    Department of Civil and Environmental Engineering, Nanyang Technological University, Singapore
  • Zehra Sarac
    Department of Electrical and Electronics Engineering, Bulent Ecevit University, Zonguldak, Turkey
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
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Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.
Introduction
Generally, unsupervised learning or self-organized learning finds regularities in the data represented by the examples. Clustering methods such as model-based, density based and user guided methods are often applied for data reduction such as summarization like preprocessing of classification; compression like vector quantization; and finding the nearest neighbors. Specifically, a feed-forward neural network is a software version of the human brain and have their roots in Hebbian and competitive learning such as Kohonen’s self-organizing map and growing neural gas. In this network, data processing has only one forward direction from the input layer to the output layer without any cycle or backward movement; and generally exhibits several advantages such as an inherent distributed parallel processing architectures, as well as capabilities to adjust the interconnection weights to learn and describe suitable clusters, process vector quantization prototypes and distribute similar data without class labels to describe the clusters, control noisy data, cluster unknown data, and learn the types of input values on the basis of their weights and properties. The current online dynamic unsupervised feed-forward neural network clustering methods such as evolving self-organizing map and dynamic self-organizing map inherit some of the advantages and disadvantages of static unsupervised feed-forward neural network clustering methods; which are suitable to be applied in different research areas such as email logs, networks, credit card transactions, astronomy and satellite communications. Generally, the critical issues of clustering are data losing, definition of clustering principles, number and Unsupervised clustering is a valuable subject to research, however, their critical issues are data losing, definition of clustering principles, number and densities of clusters. Specially, the main problems in dynamic feed-forward neural network clustering are low speed, high memory usage and memory complexity through using random weights and parameters, and relearning. The goal of this research is an investigation of current unsupervised clustering and identify their limitations and problems through a literature review and experience.

Aims and Scope:

The topics of the special session include, but are not limited to:
Learning and Neural Network
Unsupervised Feed Forward Neural Network clustering
Static Unsupervised Neural Network clustering
Dynamic Unsupervised Neural Network clustering
Semi-supervised Neural Network clustering
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