Band depth based initialization of K-means for functional data clustering

DOI10.1007/s11634-022-00510-warXiv2106.01129OpenAlexW3169881201MaRDI QIDQ6106174

Javier Albert-Smet, Aurora Torrente, Juan J. Romo

Publication date: 27 June 2023

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.01129

zbMATH Keywords

bootstrapB-splinefunctional data\(k\)-meansmodified band depth

Mathematics Subject Classification ID

Functional data analysis (62R10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

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• \(k\)-mean alignment for curve clustering
• The plane with parallel coordinates
• Initializing \(k\)-means clustering by bootstrap and data depth
• Functional data clustering: a survey
• Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
• Robust depth-based estimation in the time warping model
• K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
• A Comparison of Hierarchical Methods for Clustering Functional Data
• Finding Groups in Data
• Curve Registration
• Unsupervised Curve Clustering using B‐Splines
• On the Concept of Depth for Functional Data
• Analysis of Variance for Functional Data

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