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. • Provides open-source Python code for multi-view intuitionistic fuzzy clustering. • Implements adaptive weighting for views, features, and samples in clustering. • Includes neighborhood-aware clustering to handle noise and improve robustness. • Initial centroids are selected via a density-based strategy to ensure stability. • Code is modular, well-documented, and publicly available on GitHub for use.