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The clustering of data procedure entails grouping the data objects into different teams according to shared characteristics. Recently, the Bacterium Colony Improvement (BCI) technique has been applied to overcome a data clustering issue. Yet, the conjunction rate of BCI is extremely low. In our article, an opposition-based learning technique (OBL + BCI) that increases the effectiveness of the data clustering problem and speeds up BCI’s convergence rate is presented. The efficiency of the suggested data clustering strategy is assessed using 10 renowned UCI machine learning example data. The results of the experiment reveal that the OBL + BCI data clustering strategy delivered superior outcomes when compared to existing data clustering techniques.

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Bacterium Colonies with Improved Opposition Based Learning for Medical Data Clustering

  • 章节

  • 发表日期：

    2026年

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  • V. S. Prakash,
  • B. N. Shivakumar,
  • B. Vinoth,
  • G. Prathap,
  • J. Vennila,
  • P. Natarajan

摘要

The clustering of data procedure entails grouping the data objects into different teams according to shared characteristics. Recently, the Bacterium Colony Improvement (BCI) technique has been applied to overcome a data clustering issue. Yet, the conjunction rate of BCI is extremely low. In our article, an opposition-based learning technique (OBL + BCI) that increases the effectiveness of the data clustering problem and speeds up BCI’s convergence rate is presented. The efficiency of the suggested data clustering strategy is assessed using 10 renowned UCI machine learning example data. The results of the experiment reveal that the OBL + BCI data clustering strategy delivered superior outcomes when compared to existing data clustering techniques.