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

Conference paper
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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 5)

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.

Keywords

Clustering K-means Differential evolution Spiking neural network Evolving spiking neural networks K-DESNN 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.FSKPM FacultyUniversity Malaysia Sarawak (UNIMAS)Kota SamarahanMalaysia
  2. 2.Soft Computing Research Group, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  3. 3.UTM Big Data CentreUniversiti Teknologi MalaysiaSkudaiMalaysia
  4. 4.Faculty of Computer and TechnologyAlzaiem Alazhari UniversityKhartoumSudan
  5. 5.Nile CollegeKhartoum NorthSudan

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