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An Efficient Approach for Selection of Initial Cluster Centroids for k-means

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Data Science and Analytics (REDSET 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1229))

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Abstract

Choice of initial centroids has a major impact on the performance and accuracy of k-means algorithm to group the data objects into various clusters. In basic k-means, pure arbitrary choice of initial centroids lead to construction of different clusters in every run and consequently affects the performance and accuracy of it. To date, several attempts have been made by the researchers to increase the performance and accuracy of it. However, scope of improvement still exists in this area. Therefore, a new approach to initialize centroids for k-means is proposed in this paper on the basis of the concept to choose the well separated data-objects as initial cluster centroids instead of pure arbitrary selection. As a consequence, it leads to higher probability of closeness of the chosen centroids to the final cluster centroids. The proposed algorithm is empirically assessed on 6 different well-known datasets. The results confirms that the proposed approach is considerably better than the pure arbitrary selection of centroids.

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Authors and Affiliations

  1. USIC&T, Guru Gobind Singh Indraprastha University, Dwarka, India

    Manoj Kr. Gupta & Pravin Chandra

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  1. Manoj Kr. Gupta
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  2. Pravin Chandra
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Correspondence to Manoj Kr. Gupta .

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Editors and Affiliations

  1. GD Goenka University, Gurugram, India

    Usha Batra

  2. GD Goenka University, Sohna, Haryana, India

    Nihar Ranjan Roy

  3. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

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Gupta, M.K., Chandra, P. (2020). An Efficient Approach for Selection of Initial Cluster Centroids for k-means. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_1

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  • DOI: https://doi.org/10.1007/978-981-15-5827-6_1

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