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arXiv · 2501.10471

VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications

Aditya Ballal·Gregory A. DePaul·Esha Datta·Asuka Hatano·Erik Carlsson·Ye Chen-Izu·Javier E. López·Leighton T. Izu
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Abstract

Clustering large high-dimensional datasets with diverse variable is essential for extracting high-level latent information from these datasets. Here, we developed an unsupervised clustering algorithm, we call "Village-Net". Village-Net is specifically designed to effectively cluster high-dimension data without priori knowledge on the number of existing clusters. The algorithm operates in two phases: first, utilizing K-Means clustering, it divides the dataset into distinct subsets we refer to as "villages". Next, a weighted network is created, with each node representing a village, capturing their proximity relationships. To achieve optimal clustering, we process this network using a community detection algorithm called Walk-likelihood Community Finder (WLCF), a community detection algorithm developed by one of our team members. A salient feature of Village-Net Clustering is its ability to autonomously determine an optimal number of clusters for further analysis based on inherent characteristics of the data. We present extensive benchmarking on extant real-world datasets with known ground-truth labels to showcase its competitive performance, particularly in terms of the normalized mutual information (NMI) score, when compared to other state-of-the-art methods. The algorithm is computationally efficient, boasting a time complexity of O(N*k*d), where N signifies the number of instances, k represents the number of villages and d represents the dimension of the dataset, which makes it well suited for effectively handling large-scale datasets.

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BibTeXRIS

Aditya Ballal, Gregory A. DePaul, Esha Datta, Asuka Hatano, Erik Carlsson, Ye Chen-Izu, Javier E. López, Leighton T. Izu. 2026-02-23. VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications. https://arxiv.org/abs/2501.10471

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