A New Framework for Distance-based Functional Clustering

Al Alawi, M., Ray, S. and Gupta, M. (2019) A New Framework for Distance-based Functional Clustering. In: 34th International Workshop on Statistical Modelling, Guimarães, Portugal, 07-12 Jul 2019,

191547.pdf - Accepted Version


Publisher's URL: http://www.iwsm2019.org/


We develop a new framework for clustering functional data, based on a distance matrix similar to the approach in clustering multivariate data using spectral clustering. First, we smooth the raw observations using appropriate smoothing techniques with desired smoothness, through a penalized fit. The next step is to create an optimal distance matrix either from the smoothed curves or their available derivatives. The choice of the distance matrix depends on the nature of the data. Finally, we create and implement the spectral clustering algorithm. We applied our newly developed approach, Functional Spectral Clustering (FSC) on sets of simulated and real data. Our proposed method showed better performance than existing methods with respect to accuracy rates.

Item Type:Conference Proceedings
Keywords:Functional data, smoothing, clustering, spectral clustering.
Glasgow Author(s) Enlighten ID:Al Alawi, Maryam Ali Said and Gupta, Dr Mayetri and Ray, Professor Surajit
Authors: Al Alawi, M., Ray, S., and Gupta, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Proceedings of the 34th International Workshop on Statistical Modelling
Publisher Policy:Reproduced with the permission of the Authors

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