From the 1 of 5 linked papers with an AI index.
5 papers
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.
Crowdsourcing Without People: Modelling Clustering Algorithms as Experts
Jordyn E. A. Lorentz, Katharine M. Clark
This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional c…
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…
Finding Outliers in Gaussian Model-Based Clustering
Katharine M. Clark, Paul D. McNicholas
Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithm…