Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations

Year : 2025 | Volume : 03 | Issue : 01 | Page : 30 37
    By

    Sohan Lal Gupta,

  • Vinod Kataria,

  • Arpita Sharma,

  • Vikram Khandelwal,

  • Anjali Pandey,

  • Vipin Kumar Gupta,

  1. Assistant Professor, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  2. Associate Professor, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  3. Assistant Professor, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  4. Assistant Professor, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  5. Assistant Professor, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  6. Assistant Professor, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

Abstract

K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the chances of poor clustering results by selecting more optimal starting centroids. Additionally, optimization techniques, such as using advanced optimization algorithms and parallel processing, have been developed to accelerate convergence and handle larger datasets more efficiently. We also explore hybrid approaches that combine K-Means with other clustering algorithms to achieve more accurate and robust clustering outcomes. These advancements collectively contribute to the enhanced performance, scalability, and robustness of the K-Means algorithm, making it more suitable for a wider range of applications in data analysis and machine learning.

Keywords: Data mining, data clustering, centroids, SSE, k-means, distance metrices

[This article belongs to International Journal of Solid State Innovations & Research ]

How to cite this article:
Sohan Lal Gupta, Vinod Kataria, Arpita Sharma, Vikram Khandelwal, Anjali Pandey, Vipin Kumar Gupta. Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations. International Journal of Solid State Innovations & Research. 2025; 03(01):30-37.
How to cite this URL:
Sohan Lal Gupta, Vinod Kataria, Arpita Sharma, Vikram Khandelwal, Anjali Pandey, Vipin Kumar Gupta. Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations. International Journal of Solid State Innovations & Research. 2025; 03(01):30-37. Available from: https://journals.stmjournals.com/ijssir/article=2025/view=206742


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Regular Issue Subscription Original Research
Volume 03
Issue 01
Received 24/03/2025
Accepted 01/04/2025
Published 09/04/2025
Publication Time 16 Days
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