Validating visual clusters in large datasets: fixed point clusters of spectral features.

DOI10.1016/S0167-9473(02)00077-4zbMath1043.68089WikidataQ60471688 ScholiaQ60471688MaRDI QIDQ1852887

Norbert Christlieb, Christian Hennig

Publication date: 21 January 2003

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Data visualizationOutliersClassification plotsContamination modelDiscriminant coordinatesSky surveysStellar populations

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Computing methodologies and applications (68U99)

Related Items

Clusters, outliers, and regression: Fixed point clusters, Robust Linear Clustering, Distance-based parametric bootstrap tests for clustering of species ranges, Experiments with, and on, algorithms for maximum likelihood clustering, Exploring the number of groups in robust model-based clustering, The importance of the scales in heterogeneous robust clustering

Uses Software

• XGobi
• clusfind

Cites Work

• A procedure for the detection of multivariate outliers.
• Visual clustering and classification: The Oronsay particle size dataset revisited
• Trimmed \(k\)-means: An attempt to robustify quantizers
• The Grand Tour: A Tool for Viewing Multidimensional Data
• Finding Groups in Data
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• The Masking Breakdown Point of Multivariate Outlier Identification Rules
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item