arXiv:2607. 19089v1 Announce Type: new Abstract: Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide.
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...
arXiv:2511. 17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning.
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.
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.
arXiv:2608. 06990v1 Announce Type: cross Abstract: Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning.
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...
arXiv:2604. 07085v2 Announce Type: replace Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making.
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.
The paper presents a new association rule-based data mining technique for classifying breast cancer, emphasizing early detection. It introduces a weighted classification approach that uses three core algorithms: Rule Generation, Rule Pruning, and Rule Prediction. The method identifies frequent itemsets, prunes rules into major and minor groups, and applies the pruned rules to classify test data, demonstrating feasibility and performance on several samples.
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.
arXiv:2509. 04222v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure.
arXiv:2606. 00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries.