An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling
Annals of Data Science
; 2022.
Article
in English
| Scopus | ID: covidwho-1920411
ABSTRACT
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. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Data Science; K-means Clustering; Machine Learning; Percentile; Principal Component Analysis; Unsupervised Algorithm; Iterative methods; Learning algorithms; Data-driven model; K-mean algorithms; K-means clustering algorithms; K-means++ clustering; Machine-learning; Number of iterations; Principal-component analysis; Unsupervised algorithms; Unsupervised machine learning
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Annals of Data Science
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS