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. 07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols.
By Zihua Yang, Zhencheng Xie, Junyang Chen, Liang Xie, Yiqun Zhang, Mengke Li, Yang Lu
The paper introduces SD-Pcomp learning, a binary classification framework that jointly utilizes Similarity/Dissimilarity (SD) labels and Pairwise Comparison (Pcomp) labels from instance pairs. It proposes an objective function that can be decomposed into either an SD estimator plus ordering information or a Pcomp estimator plus pair-type information, thereby integrating complementary relational cues. Experiments on eight datasets demonstrate that combining both label types improves classification accuracy and AUC compared to using either alone or a simple convex combination.
By Tomoya Tate, Kosuke Sugiyama, Masato Uchida
arXiv:2511. 03000v2 Announce Type: replace-cross Abstract: Comparing clusterings is central to evaluating unsupervised models, yet the many existing similarity measures can produce widely divergent, sometimes contradictory, evaluations.
By Alexander J. Gates
arXiv:2606. 16379v1 Announce Type: new Abstract: Evaluating representation similarity is fundamental to representation learning.
By Diogo Soares, Pankhil Gawade, Andrea Dittadi, Ewa Szczurek
The paper extends the h‑plot multidimensional scaling technique to handle three‑way asymmetric dissimilarities, enabling the extraction of archetypal profiles and clustering in a unified Euclidean space. It provides an explicit eigenvector‑based solution that avoids local minima, is scale‑invariant, computationally efficient, and includes a straightforward goodness‑of‑fit assessment. The method is benchmarked against existing models and demonstrated on a financial dataset, with all data and code made publicly available for reproducibility.
By Mireia Mollar-Gumbau, Aleix Alcacer, Rafael Benitez, Vicente J. Bolos, Irene Epifanio