arXiv:2607. 05464v1 Announce Type: cross Abstract: The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 06990v1 Announce Type: cross Abstract: Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning.
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.
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.
arXiv:2606. 16379v1 Announce Type: new Abstract: Evaluating representation similarity is fundamental to representation learning.
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.
arXiv:2511. 17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning.