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

• Main Authors: Abdulrazak Yahya, Saleh, Haza Nuzly, Abdull Hamed, Siti Mariyam, Shamsuddin, Ashraf, Osman Ibrahim
• Format: Book Chapter
• Language: English
• Published: Springer International Publishing 2018
• Subjects: H Social Sciences (General); QA75 Electronic computers. Computer science
• 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

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.