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Implementation of Clustering using K-Means in Python

by Ahmad Farhan AlShammari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 40
Year of Publication: 2024
Authors: Ahmad Farhan AlShammari
10.5120/ijca2024923990

Ahmad Farhan AlShammari . Implementation of Clustering using K-Means in Python. International Journal of Computer Applications. 186, 40 ( Sep 2024), 12-17. DOI=10.5120/ijca2024923990

@article{ 10.5120/ijca2024923990,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Clustering using K-Means in Python },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 40 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number40/implementation-of-clustering-using-k-means-in-python/ },
doi = { 10.5120/ijca2024923990 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-27T00:46:19+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Clustering using K-Means in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 40
%P 12-17
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to develop a clustering program using k-means method in Python. Clustering helps to divide data into clusters (or groups) based on their features. K-means is used to assign the data points to the cluster of the closest center. Euclidean distance is used to measure the distances between the data points and the centers. K-means is an iterative method that continues in processing to update the centers until the final clusters are obtained. The basic steps of clustering using k-means are explained: preparing data, initializing centers, computing labels (computing distances, finding minimum distance, and assigning labels), computing clusters, computing error function, updating centers, and plotting clusters. The developed program was tested on an experimental dataset. The program successfully performed the basic steps of clustering using k-means and provided the required results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Clustering K-Means Euclidean Distance Centers Labels Clusters Error Function Python Programming.

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