International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 3 (March 2023), Pages: 189-195

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 Original Research Paper

 Bootstrap approach for clustering method with applications

 Author(s): 

 Sulafah M. Saleh Binhimd, Zakiah I. Kalantan *

 Affiliation(s):

 Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7040-5623

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.03.023

 Abstract:

Discovering patterns of big data is an important step to actionable insights data. The clustering method is used to identify the data pattern by splitting the data set into clusters with associated variables. Various research works proposed a bootstrap method for clustering the array data but there is a weak view of statistical or theoretical results and measures of the model consistency or stability. The purpose of this paper is to assess model stability and cluster consistency of the K-number of clusters by using bootstrap sampling patterns with replacement. In addition, we present a reasonable number of clusters via bootstrap methods and study the significance of the K-number of clusters for the original data set by looking at the value of the K-number that provides the most stable clusters. Practically, bootstrap is used to measure the accuracy of estimation and analyze the stability of the outcomes of cluster methods.  We discuss the performance of suggestion clusters through running examples. We measure the stability of clusters through bootstrap. A simulation study is presented in order to illustrate the methods of inference discussed and examine the satisfactory performance of the proposed distributions.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Bootstrap method, K-means method, Cluster method, Parametric and semi-parametric methods

 Article History: Received 28 June 2022, Received in revised form 24 November 2022, Accepted 26 December 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Binhimd SMS and Kalantan ZI (2023). Bootstrap approach for clustering method with applications. International Journal of Advanced and Applied Sciences, 10(3): 189-195

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 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

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