A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution

Abdulrazak Yahya, Saleh Al-Hababi and Haza Nuzly, Abdull Hamed and Siti Mariyam, Binti Shamsuddin and Ashraf, Osman Ibrahim (2018) A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution. In: Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies . Springer International Publishing, pp. 571-583. ISBN 978-3-319-59427-9

[img] PDF
A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution - Copy.pdf

Download (98MB)
Official URL: https://link.springer.com/chapter/10.1007/978-3-31...

Abstract

Clustering is one of the essential unsupervised learning techniques in Data Mining. In this paper, a new hybrid (K-DESNN) approach to combine differential evolution and K-means evolving spiking neural network model (K-means ESNN) for clustering problems has been proposed. The proposed model examines that ESNN improves by using K-DESNN model. This approach improves the flexibility of the ESNN algorithm in producing better solutions which is utilized to conquer the K-means disadvantages. Various UCI machine learning data sets have been utilized for evaluating the performance of this model. The results have shown that K-DESNN is much better than the original K-means ESNN in the number of pre-synaptic neurons measure and clustering accuracy performance.

Item Type: Book Section
Uncontrolled Keywords: Clustering � K-means � Differential evolution � Spiking neural network � Evolving spiking neural networks � K-DESNN, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak.
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Gani
Date Deposited: 27 Dec 2018 08:59
Last Modified: 31 Jul 2019 08:10
URI: http://ir.unimas.my/id/eprint/22920

Actions (For repository members only: login required)

View Item View Item