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Research on data visualization technology of logistics distribution system based on clustering algorithm

Published: 23 February 2019 Publication History

Abstract

This paper takes the logistics distribution record of Yifeng Weiye Group for the past two years as the basic research unit. By exploring the relationship between data fields, we use the idea of adaptive clustering algorithm and spatial clustering analysis to process the attribute data of transportation capacity[8]. Basing on the obtained clustering results, we use Python and PHP technology to optimize the distribution area, and finally design an effective visual expression method to obtain the traffic situation knowledge. We can provide relevant analysis and technical support for enterprises to improve the efficiency of distribution logistics and optimize the structure of the industrial chain.

References

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Kulczycki P, Charytanowicz M, and Kowalski P A, et al, Exemplary applications of the complete gradient clustering algorithm in bioinformatics, management and engineering{M}, Issues and Challenges of Intelligent Systems and Computational Intelligence, Springer International Publishing, 2014: 119--132.
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Yu S S, Chu S W, and Wang C L, et al, A modified K-means algorithms-Bi-Level K-means algorithm{C}. 2nd International Conference on Soft Computing in Information Communication Technology. Atlantis Press, 2014.
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Zeyu Li. Research on Spatial Distribution Model Based on Spatial Statistical Cluster Analysis and GIS{J}. Surveying and Spatial Geography Information, 2018.9: 171--177.
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Hao Yang. Application of SPSS-based clustering analysis in industry statistics{D}. Changchun: Jilin University, 2013.
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Xiaoxia Sun. Application Research of Cluster Analysis in Customer Segmentation{D}. Northwest University, 2006.
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Wenchong Zhao, Jianghui Cai, Xujun Zhao, and Jifu Zhang. A fast K_means clustering algorithm under the influence space{J}. Small Computer System, 2016.9: 2060--2064.
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Qingquan Li, and Deren Li. Big Data GIS {J}. Journal of Wuhan University: Information Science Edition, 2014, 39(6): 641--644

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    ICIGP '19: Proceedings of the 2nd International Conference on Image and Graphics Processing
    February 2019
    151 pages
    ISBN:9781450360920
    DOI:10.1145/3313950
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 February 2019

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    Author Tags

    1. clustering algorithm
    2. heat map
    3. spatial clustering analysis

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