Title: Density peaks clustering algorithm based on natural nearest neighbours and its application in network advertising recognition

Authors: Zhanfeng Yao; Tanghuai Fan; Xin Li; Jia Zhao

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330000, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330000, China

Abstract: For the density peaks clustering algorithm, when dealing with multi-scale and manifold datasets, the algorithm cannot find the correct density peaks, and the distribution strategy is prone to the cascading effect of distribution errors. Hence, we propose a density peaks clustering algorithm based on natural nearest neighbours. In the proposed algorithm, the natural nearest neighbours of the sample are taken as the initial density by Gauss summation, and then it compares this initial density with the initial density of its k-nearest neighbours to form the final local density, the natural nearest neighbour information used to strengthen the relationship between samples, redefine the similarity measure between samples, and use it for remaining sample allocation. The experimental results on the synthetic datasets and network advertising recognition dataset show that the clustering effect of the proposed algorithm is better than that of DPC, DPCSA and FNDPCs, and more accurate advertising screening effect is achieved.

Keywords: density peaks clustering; natural nearest neighbours; local density; distribution strategy; network advertising recognition.

DOI: 10.1504/IJWMC.2020.106775

International Journal of Wireless and Mobile Computing, 2020 Vol.18 No.3, pp.266 - 276

Received: 22 Nov 2019
Accepted: 26 Dec 2019

Published online: 31 Mar 2020 *

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