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

PLoS One. 2018 Dec 17;13(12):e0200751. doi: 10.1371/journal.pone.0200751. eCollection 2018.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Evolution, Molecular*
  • Models, Genetic*
  • Promoter Regions, Genetic*
  • Sequence Analysis, DNA / methods*

Grants and funding

This work was supported by National Natural Science Foundation of China (No.61806114 to Y. Zhao, No. 61472231 to X. Liu, No. 61876101 to X. Liu, No. 61602282, No. 61402187, No. 61502283, No. 61802234, No.61703251, http://www.nsfc.gov.cn/), China Postdoctoral Science Foundation (No.2018M642695 to Y. Zhao, http://jj.chinapostdoctor.org.cn/V1/Program3/Default.aspx) and the Natural Science Foundation of Shandong Province (No. ZR2016AQ21, http://www.sdnsf.gov.cn/portal/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.