Performances of k-means clustering algorithm with different distance metrics

dc.contributor.author Ghazal, Taher M.
dc.contributor.author Hussain, Muhammad Zahid
dc.contributor.author Said, Raed A.
dc.contributor.author Nadeem, Afrozah
dc.contributor.author Hasan, Mohammad Kamrul
dc.contributor.author Ahmad, Munir
dc.contributor.author Khan, Muhammad Adnan
dc.contributor.author Naseem, Muhammad Tahir
dc.date.accessioned 2021-09-12T12:10:43Z
dc.date.available 2021-09-12T12:10:43Z
dc.date.issued 2021
dc.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 en_US
dc.description.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. en_US
dc.identifier.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 en_US
dc.identifier.issn 10798587
dc.identifier.uri https://doi.org/10.32604/iasc.2021.019067
dc.identifier.uri http://hdl.handle.net/20.500.12519/440
dc.language.iso en en_US
dc.publisher Tech Science Press en_US
dc.relation Authors Affiliations : Ghazal, T.M., Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, Selangor, 43600, Malaysia, School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Hussain, M.Z., Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan; Said, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Nadeem, A., Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan; Hasan, M.K., Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, Selangor, 43600, Malaysia; Ahmad, M., School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan; Khan, M.A., Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan, Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Seongnam, 13557, South Korea; Naseem, M.T., Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
dc.relation.ispartofseries Intelligent Automation and Soft Computing ; Volume 30, Issue 2
dc.rights Creative Commons Attribution 4.0 International License
dc.rights.holder Copyright : © 2021, Tech Science Press. All rights reserved.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Distance metrics en_US
dc.subject Euclidean distance en_US
dc.subject K-means clustering en_US
dc.subject Manhattan distance
dc.subject Minkowski distance
dc.title Performances of k-means clustering algorithm with different distance metrics en_US
dc.type Article en_US
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