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All Journal Data Science Insights
Daud, Hanita Binti Daud
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Computer Science & IT Engineering

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Assessing the Efficiency and Accuracy of K-Means Clustering Compared to Other Clustering Techniques Khan, Iliyas; Daud, Hanita Binti Daud; Zainuddin , Nooraini binti Zainuddin; Sokkalingam, Rajalingam Sokkalingam; Azad , Abdus Samad Azad; Samad, Abdussamad; Suleiman, Ahmad Abubakar Suleiman
Data Science Insights Vol. 3 No. 2 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i2.23

Abstract

Clustering is an important method in data analysis, faces challenges due to the different nature of datasets, resulting in certain algorithms being less effective and taking a long time. Choosing the most effective clustering method involves evaluating its accuracy and computational speed for a dataset poses a significant challenge for today's researchers. To address these issues, current study compares different clustering methods, by using datasets, including iris, seed, and well log to evaluate their accuracy and execution speed. Results show that K-means performs better with large datasets. As sample size increases, the accuracy of the K-means algorithm tends to improve. The execution time of k-means is influenced by the number of features in the dataset, with datasets having a larger number of features typically requiring more time to process. Mean shift algorithm and spectral clustering algorithm are performed well in small data sets, but it takes a long time.
Co-Authors Azad , Abdus Samad Azad Khan, Iliyas Samad, Abdussamad Sokkalingam, Rajalingam Sokkalingam Suleiman, Ahmad Abubakar Suleiman Zainuddin , Nooraini binti Zainuddin