Identifying cluster number for subspace projected functional data clustering

DOI10.1016/j.csda.2011.01.001zbMath1328.62387OpenAlexW2059715093MaRDI QIDQ901600

Pai-Ling Li, Jeng-Min Chiou

Publication date: 12 January 2016

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

Full work available at URL: https://doi.org/10.1016/j.csda.2011.01.001

zbMATH Keywords

hypothesis testcluster analysisfunctional data analysisbootstrappinggene expression profilesfunctional principal components

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (10)

Principal components for multivariate functional data ⋮ Functional data analysis in shape analysis ⋮ Functional data classification using covariate-adjusted subspace projection ⋮ A self-organizing clustering algorithm for functional data ⋮ Spatial functional normal mixed effect approach for curve classification ⋮ Dynamical functional prediction and classification, with application to traffic flow prediction ⋮ Functional principal component analysis of spatially correlated data ⋮ Functional factor analysis for periodic remote sensing data ⋮ Unnamed Item ⋮ Supervised classification for functional data: a weighted distance approach

Uses Software

• Silhouettes
• bootstrap

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• A proposal for robust curve clustering
• A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
• Simultaneous curve registration and clustering for functional data
• \(k\)-mean alignment for curve clustering
• Robust estimation and classification for functional data via projection-based depth notions
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• On the number of clusters
• Nonparametric functional data analysis. Theory and practice.
• Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate
• Time-synchronized clustering of gene expression trajectories
• Functional Clustering by Bayesian Wavelet Methods
• A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering
• Penalized Clustering of Large-Scale Functional Data With Multiple Covariates
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Unsupervised Curve Clustering using B‐Splines
• Clustering for Sparsely Sampled Functional Data
• Finding the Number of Clusters in a Dataset
• Functional Clustering and Identifying Substructures of Longitudinal Data
• Correlation-Based Functional Clustering via Subspace Projection
• Determining the Number of Clusters Using the Weighted Gap Statistic
• Functional Data Analysis for Sparse Longitudinal Data
• CATS

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