Authors:
Kyösti Karttunen
;
Lasse Holmström
and
Jussi Klemelä
Affiliation:
University of Oulu, Finland
Keyword(s):
Explorative Data Analysis, Flow Cytometry, Kernel Density Estimation, Level Set Tree, Marginal Density, Mode Detection.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Information and Scientific Visualization
;
Visual Data Analysis and Knowledge Discovery
Abstract:
We study level set tree methods to analyze and visualize multivariate data. The probability density function of the underlying distribution is estimated using a kernel density estimator, and the density estimate is visualized using level set trees. These trees can be used to analyze the mode structure of a function. We show how level set trees can be used to enhance more traditional density function visualization tools, like marginal densities and slices of the density. The method is applied to flow cytometry data.