Functional data clustering: a survey

Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are proposed. The first group consists of methods working directly on the evaluation points of the curves. The second groups is defined by filtering methods which first approximate the cu...

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Bibliographic Details
Published in:Advances in data analysis and classification Vol. 8; no. 3; pp. 231 - 255
Main Authors: Jacques, Julien, Preda, Cristian
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2014
Springer Verlag
Subjects:
Chemistry and Earth Sciences
Computer Science
Data Mining and Knowledge Discovery
Economics
Finance
Health Sciences
Humanities
Insurance
Law
Management
Mathematics
Mathematics and Statistics
Medicine
Physics
Regular Article
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Statistics Theory
62M99
Basis expansion
Functional data
62-07
62H30
Clustering
Functional principal component analysis
ISSN:1862-5347, 1862-5355
Online Access:Get full text
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Summary:Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are proposed. The first group consists of methods working directly on the evaluation points of the curves. The second groups is defined by filtering methods which first approximate the curves into a finite basis of functions and second perform clustering using the basis expansion coefficients. The third groups is composed of methods which perform simultaneously dimensionality reduction of the curves and clustering, leading to functional representation of data depending on clusters. The last group consists of distance-based methods using clustering algorithms based on specific distances for functional data. A software review as well as an illustration of the application of these algorithms on real data are presented.
ISSN:1862-5347
1862-5355
DOI:10.1007/s11634-013-0158-y