A novel centroids initialisation for K-means clustering in the presence of benign outliers

Article


Karami, A., Ur Rehman, S. and Ghazanfar, M. 2020. A novel centroids initialisation for K-means clustering in the presence of benign outliers. International Journal of Data Analysis Techniques and Strategies. 12 (4), pp. 287-298. https://doi.org/10.1504/IJDATS.2020.111498
AuthorsKarami, A., Ur Rehman, S. and Ghazanfar, M.
Abstract

K-means is one of the most important and widely applied clustering algorithms in learning systems. However, it suffers from centroids initialisation that makes K-means algorithm unstable. The performance and the stability of the K-means algorithm may be degraded if benign outliers (i.e., long-term independence data points) appear in data. In this paper, we developed a novel algorithm to optimise K-means performance in the presence of benign outliers. We firstly identified the benign outliers and executed K-means across them, then K-means runs over all data points to re-locate clusters' centroids, providing high accuracy. The experimental results over several benchmarking and synthetic datasets confirm that the proposed method significantly outperformed some existing approaches with better accuracy based on applied performance metrics.

Keywordsclustering; K-means; centroid initialisation; benign outlier
JournalInternational Journal of Data Analysis Techniques and Strategies
Journal citation12 (4), pp. 287-298
ISSN 1755-8050
Year2020
PublisherInderscience
Accepted author manuscript
License
CC BY-NC-ND
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1504/IJDATS.2020.111498
Publication dates
Online25 Nov 2020
Publication process dates
Deposited04 Jul 2023
Copyright holder© 2023, The Author
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