4 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
FastManly: An EM-Gradient Algorithm for Manly Mixture Models
Katharine M. Clark, Paul D. McNicholas
A faster implementation of mixtures of Manly transformations is proposed. This method, called FastManly, uses Newton's method for optimization in an EM gradient algorithm instead o…
Clustering Three-Way Data with Outliers
Katharine M. Clark, Paul D. McNicholas
Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, thereby making it possible to analyze data in matrix form with complex struc…
Assessing and Visualizing Matrix Variate Normality
Nikola Pocuca, Michael P. B. Gallaugher, Katharine M. Clark +1
A framework for assessing the matrix variate normality of three-way data is developed. The framework comprises a visual method and a goodness of fit test based on the Mahalanobis s…
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…