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An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors

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  • Yuzhen Zhao
  • Xiyu Liu
  • Xiufeng Li

Abstract

Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models.

Suggested Citation

  • Yuzhen Zhao & Xiyu Liu & Xiufeng Li, 2018. "An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0200751
    DOI: 10.1371/journal.pone.0200751
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    References listed on IDEAS

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    1. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
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