A Novel K-Means Evolving Spiking Neural Network Model for Clustering Problems

  • Haza Nuzly Abdull HamedEmail author
  • Abdulrazak Yahya Saleh
  • Siti Mariyam Shamsuddin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


In this paper, a novel K-means evolving spiking neural network (K-ESNN) model for clustering problems has been presented. K-means has been utilised to improve the original ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions to overcoming 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 the K-ESNN provides competitive results in clustering accuracy and speed performance measures compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems.


Clustering Evolving Spiking Neural Networks K-ESNN K-means Spiking Neural Network 


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Authors and Affiliations

  • Haza Nuzly Abdull Hamed
    • 1
    Email author
  • Abdulrazak Yahya Saleh
    • 2
  • Siti Mariyam Shamsuddin
    • 2
  1. 1.Soft Computing Research Group, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  2. 2.UTM Big Data CentreUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

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