skip to main content
10.1145/3640912.3640969acmotherconferencesArticle/Chapter ViewAbstractPublication PagescnmlConference Proceedingsconference-collections
research-article

Classification model based on improved K-means clustering algorithm

Published: 22 February 2024 Publication History

Abstract

The traditional k-mean clustering algorithm has some drawbacks, such as the need to manually determine the initial K value in advance, and the value may not match the real data distribution, and is susceptible to noise, thus causing classification errors. In this paper, we take the lead in improving the contour coefficient solving code and the k-mean clustering algorithm, so that the two improved codes can cooperate with each other to improve the accuracy of the results of the algorithm.
Firstly, we use variance comparison to sub-classify the dataset, aiming to find the initial clustering center of the dataset, use the k-means clustering algorithm to classify the dataset, and finally introduce the contour coefficient to evaluate the classification results. Finally, we apply this algorithm to an example of artifact classification and train the improved algorithm with a large amount of data, and the results demonstrate that the contour coefficient-k-mean clustering algorithm yields high accuracy in the classification results.

References

[1]
A review of sensitivity analysis. Cai Yi, Xing Yan, Hu Dan. Journal of Beijing Normal University (Natural Science Edition), 2008: 44(1): p.9-16.
[2]
Wang Chengxuan. Tao Ying. Weathering of silicate glasses. Journal of Silicates. 2003, 1.
[3]
Landy, N. I, Sajuyigbe, S., Mock, J. J., Smith, D. R. & Padilla, W. J. The perfect metamaterial absober. Phys. Rev. Lett. 100, 207402, 2008.
[4]
Ren Zhanzhan. A study of glassware from the Wei, Jin, North and South Dynasties, Sui and Tang. Suzhou University. 2021, 2, 16.
[5]
Huang Qisan. A Review of the Chemical Composition of Ancient Glasses of the World. Lett. 16, 784–787, 2017.
[6]
D.T.Pham, S S Dimov and C D NguyenView all authors and affiliations. Selectuon of K in K-means clustering Volume 219, Issue 1.A-Mater. 102, 99–103, 2011.
[7]
Ahmed, M; Seraj, R; Islam, S.M.S. The k-means Algorithm. A Comprehensive Survey and Performance Evluation. Electronics 2020, 9, 1295.
[8]
Ahmed, M; Seraj, R; Islam, S.M.S. The k-means Algorithm. A Comprehensive Survey and Performance Evluation. Electronics 2020, 9(8), 1295.
[9]
S. Na, L. Xumin and G. Yong, "Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm," 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jian, China, 2010, pp. 63-67.

Index Terms

  1. Classification model based on improved K-means clustering algorithm
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        CNML '23: Proceedings of the 2023 International Conference on Communication Network and Machine Learning
        October 2023
        446 pages
        ISBN:9798400716683
        DOI:10.1145/3640912
        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 the author(s) 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].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 22 February 2024

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. classification models
        2. k-means clustering-contour coefficient
        3. variance comparison

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        CNML 2023

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 26
          Total Downloads
        • Downloads (Last 12 months)26
        • Downloads (Last 6 weeks)4
        Reflects downloads up to 20 Feb 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media