Hamed, H. N. A. and Saleh, A. Y. and Shamsuddin, S. M. and Ibrahim, A. O. (2016) Multi-objective K-means evolving spiking neural network model based on differential evolution. In: 1st International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, ICCNEEE 2015, 7-9 Sept 2015, Khartoum, Sudan.
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Abstract
In this paper, a multi-objective K-means evolving spiking neural network (MO-KESNN) model based on differential evolution for clustering problems has been presented. K-means has been utilized to improve the ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions which is used to overcome the disadvantages of K-means. Several standard data sets from UCI machine learning are used for evaluating the performance of this model. It has been found that MO-KESNN gives competitive results in clustering accuracy performance and the number of pre-synaptic neurons measure simultaneously compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems. Clustering; Differential Evolution; Evolving Spiking Neural.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | K-means, MO-KESNN, Multi-objective |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computing |
| ID Code: | 73471 |
| Deposited By: | Mohd Zulaihi Zainudin |
| Deposited On: | 23 Nov 2017 05:09 |
| Last Modified: | 23 Nov 2017 05:09 |
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