A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution
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 examine...
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Main Authors: | , , , |
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Format: | Book Chapter |
Language: | English |
Published: |
Springer International Publishing
2018
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Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/22920/1/A%20New%20Hybrid%20K-Means%20Evolving%20Spiking%20Neural%20Network%20Model%20Based%20on%20Differential%20Evolution%20-%20Copy.pdf http://ir.unimas.my/id/eprint/22920/ https://link.springer.com/chapter/10.1007/978-3-319-59427-9_60 |
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Summary: | 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. |
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