Performances of k-means clustering algorithm with different distance metrics

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dc.contributor.authorGhazal, Taher M.
dc.contributor.authorHussain, Muhammad Zahid
dc.contributor.authorSaid, Raed A.
dc.contributor.authorNadeem, Afrozah
dc.contributor.authorHasan, Mohammad Kamrul
dc.contributor.authorAhmad, Munir
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorNaseem, Muhammad Tahir
dc.date.accessioned2021-09-12T12:10:43Z
dc.date.available2021-09-12T12:10:43Z
dc.date.issued2021
dc.description.abstractClustering 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.citationGhazal, 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.2021.019067en_US
dc.identifier.issn10798587
dc.identifier.urihttps://doi.org/10.32604/iasc.2021.019067
dc.identifier.urihttp://hdl.handle.net/20.500.12519/440
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relationAuthors 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.ispartofseriesIntelligent Automation and Soft Computing ; Volume 30, Issue 2
dc.rightsCreative Commons Attribution 4.0 International License
dc.rights.holderCopyright : © 2021, Tech Science Press. All rights reserved.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDistance metricsen_US
dc.subjectEuclidean distanceen_US
dc.subjectK-means clusteringen_US
dc.subjectManhattan distance
dc.subjectMinkowski distance
dc.titlePerformances of k-means clustering algorithm with different distance metricsen_US
dc.typeArticleen_US

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