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Haoye Qiu

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EAAI Journal 2025 Journal Article

Auto feature weighted c -means type clustering methods for color image segmentation

  • Sijia Zhu
  • Zhe Liu
  • Sukumar Letchmunan
  • Haoye Qiu

To address the limitations of existing hard c -means (HCM) and fuzzy c -means (FCM) methods, we develop four novel clustering methods: vector-weighted alternative hard c -means (VWAHCM), matrix-weighted alternative hard c -means (MWAHCM), vector-weighted alternative fuzzy c -means (VWAFCM), and matrix-weighted alternative fuzzy c -means (MWAFCM). These methods enhance clustering performance by incorporating non-Euclidean norm metrics and vector-weighted and matrix-weighted schemes without adding extra parameters. Our methods modify the traditional weight constraint from a sum to a product of weights, thereby improving robustness and accuracy. Comprehensive experiments conduct on various real-world datasets and color image segmentation tasks demonstrate the superiority of the proposed methods over traditional HCM and FCM variants. The results show significant improvements in clustering Accuracy ( A C C ), Normalized mutual information ( N M I ), Rand index ( R I ), and Fowlkes–Mallows index ( F M ). Furthermore, the proposed methods exhibit fast convergence and robust performance, proving their effectiveness in practical applications.

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ICML Conference 2025 Conference Paper

Generalization Performance of Ensemble Clustering: From Theory to Algorithm

  • Xu Zhang
  • Haoye Qiu
  • Weixuan Liang
  • Hui Liu 0032
  • Junhui Hou
  • Yuheng Jia

Ensemble clustering has demonstrated great success in practice; however, its theoretical foundations remain underexplored. This paper examines the generalization performance of ensemble clustering, focusing on generalization error, excess risk and consistency. We derive a convergence rate of generalization error bound and excess risk bound both of $\mathcal{O}(\sqrt{\frac{\log n}{m}}+\frac{1}{\sqrt{n}})$, with $n$ and $m$ being the numbers of samples and base clusterings. Based on this, we prove that when $m$ and $n$ approach infinity and $m$ is significantly larger than log $n$, i. e. , $m, n\to \infty, m\gg \log n$, ensemble clustering is consistent. Furthermore, recognizing that $n$ and $m$ are finite in practice, the generalization error cannot be reduced to zero. Thus, by assigning varying weights to finite clusterings, we minimize the error between the empirical average clusterings and their expectation. From this, we theoretically demonstrate that to achieve better clustering performance, we should minimize the deviation (bias) of base clustering from its expectation and maximize the differences (diversity) among various base clusterings. Additionally, we derive that maximizing diversity is nearly equivalent to a robust (min-max) optimization model. Finally, we instantiate our theory to develop a new ensemble clustering algorithm. Compared with SOTA methods, our approach achieves average improvements of 6. 0%, 7. 3%, and 6. 0% on 10 datasets w. r. t. NMI, ARI, and Purity. The code is available at https: //github. com/xuz2019/GPEC.

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