From the 1 of 13 linked papers with an AI index.
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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…
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…
Hidden Markov Models for Multivariate Panel Data
Mackenzie R. Neal, Alexa A. Sochaniwsky, Paul D. McNicholas
While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for…
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang +1
Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based c…
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…