Bootstrap approach for clustering method with applications
Sulafah M. Saleh Binhimd, Zakiah Ibrahim Kalantan · International Journal of ADVANCED AND APPLIED SCIENCES · 2023
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.