The paper introduces absolute cluster indices that assess both compactness and separability of clusters, moving beyond relative measures commonly used in clustering validation. It defines a compactness function for each cluster and a set of neighboring points for cluster pairs to evaluate cluster quality and overall distribution margin. These indices are applied to determine the true number of clusters and are compared against widely-used validity indices on synthetic and real-world datasets.
By Adil M. Bagirov, Ramiz M. Aliguliyev, Nargiz Sultanova, Sona Taheri
arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.
By Mahdi Shamsi, Soosan Beheshti
arXiv:2511. 17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning.
By Naitik Gada (Rochester Institute of Technology)
arXiv:2607. 20799v1 Announce Type: new Abstract: Scalar metrics are often used to evaluate clusterings against known classes, but they can obscure a fundamental trade-off: clusterings should be informative about class labels while avoiding unnecessary fragmentation.
By Andreas Tiffeau-Mayer
arXiv:2609.26748v1 Announce Type: cross
Abstract: Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters,...
By Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
arXiv:2608.30093v1 Announce Type: cross
Abstract: We introduce a robust clustering method, MK-means DPD, that estimates cluster centers and covariance matrices using density power divergence (DPD) me...
By Anirban Mondal, Paromita Banerjee, Abhijit Mandal
arXiv:2409. 00743v4 Announce Type: replace-cross Abstract: In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability.
By Lianyu Hu, Mudi Jiang, Junjie Dong, Xinying Liu, Zengyou He
The paper critiques the normalized edit distance metric used for evaluating lexicons derived from unsupervised word discovery, noting its bias toward large clusters and its failure to account for the distribution of true classes across clusters. It proposes two new metrics—one that weights cluster size when measuring within‑cluster consistency and another that evaluates how true words are spread across clusters—drawing on clustering theory. Experiments on synthetic and real‑world lexicons show that these combined metrics better correlate with ground‑truth distributions and are more robust to evaluation biases.
By Simon Malan, Danel Slabbert, Herman Kamper
arXiv:2608. 06990v1 Announce Type: cross Abstract: Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning.
By Yuning Yu, Jos\'e Rodr\'iguez-Pi\~neiro, Xuefeng Yin, Bin Feng
Gradient-Guided Density Peak Clustering (GGDPC) enhances traditional density peak clustering by performing a gradient ascent step before each nearest‑neighbor uphill search, aiming to stabilize uphill paths in low‑density regions. The authors develop a stability theory linking the GGDPC graph to the gradient ascent flow of the population density, and establish consistency across five criteria: recovery of local modes, adjusted Rand index, dendrogram (cluster tree), path length, and waterfall measure. These results offer new statistical, geometric, and topological insights into DPC‑type clustering algorithms.
By Yikun Zhang, Yen-Chi Chen
arXiv:2606. 28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems.
By Daoming Wan, Yizheng Huang, Jimmy X. Huang
arXiv:2609.36762v1 Announce Type: new
Abstract: Federated clustering methods that do not require the global number of clusters $K$ still assume that each client knows its local number $K_g$. This ass...
By Mitushi Goyal, Tarun S., Riddhanya Senapathi, Arun Raman