Performances of k-means clustering algorithm with different distance metrics

Ghazal, Taher M.; Hussain, Muhammad Zahid; Said, Raed A.; Nadeem, Afrozah; Hasan, Mohammad Kamrul; Ahmad, Munir; Khan, Muhammad Adnan; Naseem, Muhammad Tahir

Performances of k-means clustering algorithm with different distance metrics

Date

2021

Authors

Ghazal, Taher M.

Hussain, Muhammad Zahid

Said, Raed A.

Nadeem, Afrozah

Hasan, Mohammad Kamrul

Ahmad, Munir

Khan, Muhammad Adnan

Naseem, Muhammad Tahir

Publisher

Tech Science Press

Abstract

Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest cluster. Further, there are a series of distance metrics that can be applied to calculate point-to-point distances. In this research, the K-means clustering algorithm is evaluated with three different mathematical metrics in terms of execution time with different datasets and different numbers of clusters. The results indicate that the implementation of Manhattan distance measure metrics achieves the best results in most cases. These results also demonstrate that distance metrics can affect the execution time and the number of clusters created by the K-means algorithm. © 2021, Tech Science Press. All rights reserved.

Description

This article is not available at CUD collection. The version of scholarly record of this article is published in Intelligent Automation and Soft Computing (2021), available online at: https://doi.org/10.32604/iasc.2021.019067

Keywords

Distance metrics, Euclidean distance, K-means clustering, Manhattan distance, Minkowski distance

Citation

Ghazal, T. M., Hussain, M. Z., Said, R. A., Nadeem, A., Hasan, M. K., Ahmad, M., . . . Naseem, M. T. (2021). Performances of k-means clustering algorithm with different distance metrics. Intelligent Automation and Soft Computing, 30(2), 735-742. https://doi.org/10.32604/iasc.2021.019067

URI

https://doi.org/10.32604/iasc.2021.019067
http://hdl.handle.net/20.500.12519/440

Collections

International Business

Full item page