基于视点的协同特征加权多视点直觉模糊聚类的开源代码

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Amin Golzari Oskouei , Negin Samadi , Asgarali Bouyer , Jafar Tanha
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引用次数: 0

摘要

我们提出了VCoFWMVIFCM,一个基于直觉模糊c均值(IFCM)的多视图模糊聚类算法的开源Python实现。该方法集成了自适应视图、特征和样本加权,以考虑不同的重要性并减少异常值效应。局部邻域信息增强了抗噪声能力,而基于密度的初始化保证了质心选择的稳定性。这些机制共同提高了多视图数据的聚类鲁棒性和准确性。模块化实现允许灵活的执行和再现性,解决存在多个数据透视图的实际应用程序。在MIT许可下,代码可以在GitHub上公开访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VCoFWMVIFCM: An open-source code for viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering
We present VCoFWMVIFCM, an open-source Python implementation of a multi-view fuzzy clustering algorithm based on Intuitionistic Fuzzy c-Means (IFCM). The method integrates adaptive view, feature, and sample weighting to account for varying importance and reduce outlier effects. Local neighborhood information enhances noise resistance, while a density-based initialization ensures stable centroid selection. These mechanisms collectively improve clustering robustness and accuracy for multi-view data. The modular implementation allows flexible execution and reproducibility, addressing real-world applications where multiple data perspectives exist. The code is publicly accessible on GitHub under the MIT license.
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来源期刊
Software Impacts
Software Impacts Software
CiteScore
2.70
自引率
9.50%
发文量
0
审稿时长
16 days
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