Quantization based clustering: An iterative approach

•Efficient quantification based clustering.•Clustering of functional data.•Manhattan distance. In this paper we propose a simple new algorithm to perform clustering, based on the Alter algorithm proposed in [1] but lowering significantly the algorithmic complexity with respect to the number of clust...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition letters 2021-02, Vol.142, p.51-57
Main Author: Laloë, Thomas
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Machine Learning
Manhattan distance
Multivariate analysis
Quantization
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Efficient quantification based clustering.•Clustering of functional data.•Manhattan distance. In this paper we propose a simple new algorithm to perform clustering, based on the Alter algorithm proposed in [1] but lowering significantly the algorithmic complexity with respect to the number of clusters. An empirical study states the relevance of our iterative process and a confrontation on simulated multivariate and functional data shows the benefits of our algorithm.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2020.12.007