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Article

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

by 1,2, 2,3,*, 4, 1 and 2,5,*
1
International College of Digital Innovation, Chiang Mai University, Chaing Mai 50200, Thailand
2
School of Information and Engineering, Sichuan Tourism University, Chendu 610100, China
3
School of Computer Science and Technology, Aba Teachers University, Wenchuan 623002, China
4
School of Information and Engineering, Chengdu University, Chendu 610106, China
5
School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Ricardo Colomo-Palacios
Appl. Sci. 2021, 11(23), 11202; https://doi.org/10.3390/app112311202
Received: 20 October 2021 / Revised: 16 November 2021 / Accepted: 21 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Soft Computing Application to Engineering Design)
With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots. View Full-Text
Keywords: K-means clustering; noise algorithm; unsupervised evaluation; non-parametric Wilcoxon statistical analysis; urban road planning; taxi GPS data K-means clustering; noise algorithm; unsupervised evaluation; non-parametric Wilcoxon statistical analysis; urban road planning; taxi GPS data
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MDPI and ACS Style

Ran, X.; Zhou, X.; Lei, M.; Tepsan, W.; Deng, W. A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots. Appl. Sci. 2021, 11, 11202. https://doi.org/10.3390/app112311202

AMA Style

Ran X, Zhou X, Lei M, Tepsan W, Deng W. A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots. Applied Sciences. 2021; 11(23):11202. https://doi.org/10.3390/app112311202

Chicago/Turabian Style

Ran, Xiaojuan, Xiangbing Zhou, Mu Lei, Worawit Tepsan, and Wu Deng. 2021. "A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots" Applied Sciences 11, no. 23: 11202. https://doi.org/10.3390/app112311202

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