6 papers
Curve Band Depth: A Band-Based Data Depth for Unparameterized Planar Curves
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
We introduce \emph{curve band depth} (CBD), a band-based data depth for samples of \emph{unparameterized} planar curves. CBD is motivated by band depth and modified band depth for…
Automatic depth-based local center clustering via -integrated local depth and adaptive grouping
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters…
Opening the House: Datasets for Mixed Doubles Curling
Robyn Ritchie, Alexandre Leblanc, Thomas Loughin
We introduce the most comprehensive publicly available datasets for mixed doubles curling, constructed from eleven top-level tournaments from the CurlIT (https://curlit.com/results…
-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…
Curve Stabbing Depth: Data Depth for Plane Curves
Stephane Durocher, Alexandre Leblanc, Spencer Szabados
Measures of data depth have been studied extensively for point data. Motivated by recent work on analysis, clustering, and identifying representative elements in sets of trajectori…