From the 1 of 10 linked papers with an AI index.
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funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
The paper extends the OCLUST algorithm to handle functional data, providing a robust clustering method that can also identify and trim outliers in curve datasets.
Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Yicen Li, Ruiyang Hong, Anastasis Kratsios +2
Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy…
Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Mackenzie R. Neal, Paul D. McNicholas, Arthur White
Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries between clusters. Ideas from both…
An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation
Katharine M. Clark, Paul D. McNicholas
Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in…
Clustering Three-Way Data with Outliers
Katharine M. Clark, Paul D. McNicholas
Matrix-variate distributions are a recent addition to the model-based clustering field, thereby making it possible to analyze data in matrix form with complex structure such as ima…