An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One...

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Bibliographic Details
Published in:Annals of data science Vol. 11; no. 5; pp. 1525 - 1544
Main Authors: Zubair, Md, Iqbal, MD. Asif, Shil, Avijeet, Chowdhury, M. J. M., Moni, Mohammad Ali, Sarker, Iqbal H.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Business and Management
Centroids
Cluster analysis
Clustering
Datasets
Economics
Finance
Insurance
Machine learning
Management
Optimization
Statistics for Business
Subgroups
Synthetic data
Unsupervised learning
Vector quantization
Data Science
Percentile
Principal Component Analysis
Machine Learning
Unsupervised Algorithm
K-means Clustering
ISSN:2198-5804, 2198-5812, 2198-5812
Online Access:Get full text
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Summary:K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time . To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations.
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ISSN:2198-5804
2198-5812
2198-5812
DOI:10.1007/s40745-022-00428-2