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<p>Clustering is a pivotal research area in data mining and machine learning. It is integral to numerous applications. To address the limitations of single clustering algorithms, clustering ensemble techniques have been introduced to enhance clustering stability and accuracy. This paper presents a clustering ensemble method based on an improved Hamming distance metric. First, basic clustering results are generated by adjusting the parameters of the k-means algorithm. Then the improved Hamming distance is utilized to assess the differences between these basic clusters. Basic clusters exhibiting significant differences and high quality are selected for inclusion in the final integration. Subsequently, an optimized co-occurrence matrix is constructed, and the final clustering result is derived by using the normalized cut algorithm. Experimental results demonstrate that the proposed method can effectively handle multi-data datasets.</p>

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Enhanced clustering ensemble method by using improved Hamming distance

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Yuan Sun,
  • Binyao Ma,
  • Lahuan Li

摘要

Clustering is a pivotal research area in data mining and machine learning. It is integral to numerous applications. To address the limitations of single clustering algorithms, clustering ensemble techniques have been introduced to enhance clustering stability and accuracy. This paper presents a clustering ensemble method based on an improved Hamming distance metric. First, basic clustering results are generated by adjusting the parameters of the k-means algorithm. Then the improved Hamming distance is utilized to assess the differences between these basic clusters. Basic clusters exhibiting significant differences and high quality are selected for inclusion in the final integration. Subsequently, an optimized co-occurrence matrix is constructed, and the final clustering result is derived by using the normalized cut algorithm. Experimental results demonstrate that the proposed method can effectively handle multi-data datasets.