arXiv:2512.16481v2 Announce Type: replace-cross
Abstract: Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survi...
By Nora M. Villanueva, Marta Sestelo, Luis Meira-Machado
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:2606. 00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework.
By Siamak K. Sorooshyari, Manuel A. Rivas, Robert Tibshirani
The paper systematically evaluates how five dimensionality reduction methods—PCA, Kernel PCA, VAE, Isomap, and MDS—affect the performance of four clustering algorithms (k‑means, AHC, GMM, and OPTICS). Using the Adjusted Rand Index, the study compares clustering quality with and without dimensionality reduction at levels of k‑1, 25%, and 50% of the original dimensions. Results highlight that the choice of reduction technique and its level must be carefully matched to the data’s geometry and the clustering algorithm used.
By Ousmane Assani Amate, Elyes Lounissi, Mohammadreza Bakhtyari, \'Emilie Roy, Roman Sarrazin-Gendron, Vladimir Makarenkov
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
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