Rhombic Grid Based Clustering Algorithm with Spiking Neural P Systems
This paper presents a hybrid approach based on combining rhombic grids based algorithm and spiking neural P systems used for solving clustering problems(RGSNPS for short). RGSNPS uses a network membrane structure, firing and forgetting rules like in a spiking neural P system to specify
every rhombic grid respectively put inside seven cells of the SN membrane system. Each rhombic grid independently evolves in the group of cells (make up by 7 cells) according to its data contained and relationships with the connected rhombic grids. RGSNPS applies the channels connecting cells
of the SN membrane system to implement communication in the process of evolution of any two rhombic grid to do cluster analysis. An instance is used to test the performance of RGSNPS. Experiments via Iris and Wine benchmarks data sets indicate that the proposed hybrid algorithm is effectiveness
and accurate.
Keywords: CLUSTER ANALYSIS; RHOMBIC GRIDS; SPIKING NEURAL P SYSTEM
Document Type: Research Article
Publication date: 01 June 2016
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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