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A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

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  • Zhenni Jiang
  • Xiyu Liu
  • Minghe Sun

Abstract

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

Suggested Citation

  • Zhenni Jiang & Xiyu Liu & Minghe Sun, 2019. "A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:1713801
    DOI: 10.1155/2019/1713801
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