Clustering Analysis of User Loyalty Based on K-means | Zendy

Qiushui Fang | Zendy; Zhiming Li | Zendy; Mengtian Leng | Zendy; Jincheng Wu | Zendy; Zhen Wang | Zendy

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Clustering Analysis of User Loyalty Based on K-means

Author(s) -

Qiushui Fang,

Zhiming Li,

Mengtian Leng,

Jincheng Wu,

Zhen Wang

Publication year - 2020

Publication title -

journal of management science and engineering research

Language(s) - English

Resource type - Journals

ISSN - 2630-4953

DOI - 10.30564/jmser.v2i2.1851

Subject(s) - cluster analysis , kernel density estimation , swipe , loyalty , computer science , visualization , data mining , data visualization , public transport , cluster (spacecraft) , kernel (algebra) , groundwater recharge , order (exchange) , machine learning , engineering , statistics , mathematics , marketing , business , computer security , groundwater , geotechnical engineering , finance , combinatorics , estimator , transport engineering , aquifer , programming language

Article history Received: 15 May 2020 Accepted: 26 May 2020 Published Online: 30 June 2020 In recent years, the rise of machine learning has made it possible to further explore large data in various fields. In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters, this paper uses K-means clustering algorithm (K-means) to cluster the holding time, recharge amount and swiping frequency of bus travelers. Then we use Kernel Density Estimation Algorithms (KDE) to analyze the density distribution of the data of holding time, recharge amount and swipe frequency, and display the results of the two algorithms in the way of data visualization. Finally, according to the results of data visualization, the loyalty of users is classified, which provides theoretical and data support for public transport companies to determine the development potential of users.

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