4 citations · 10 across the 22 of their papers we have counts for
5 papers · 1 filter
funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the…
Model-Based Clustering with Sequential Outlier Identification using the Distribution of Mahalanobis Distances
Ultán P. Doherty, Paul D. McNicholas, Arthur White
The presence of outliers can prevent clustering algorithms from accurately determining an appropriate group structure within a data set. We present outlierMBC, a model-based approa…
-integrated local depth and corresponding partitioned local depth representation
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
A novel local depth definition, -integrated local depth (-ILD), is proposed as a generalization of the local depth introduced by Paindaveine and Van Bever \cite{paindaveine20…
Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Cluster analysis, or clustering, plays a crucial role across numerous scientific and engineering domains. Despite the wealth of clustering methods proposed over the past decades, e…
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters
Yicen Li, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios +1
In the era of pre-trained models, effective classification can often be achieved using simple linear probing or lightweight readout layers. In contrast, many competitive clustering…