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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Vydáno v:	Annals of data science Ročník 11; číslo 5; s. 1525 - 1544
Hlavní autoři:	Zubair, Md, Iqbal, MD. Asif, Shil, Avijeet, Chowdhury, M. J. M., Moni, Mohammad Ali, Sarker, Iqbal H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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