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Dynamic Response Surface Method Combined with Genetic Algorithm to Optimize Extraction Process Problem Read chapter Read first chapter

2021 | OriginalPaper | Chapter

A Simple Clustering Algorithm Based on Weighted Expected Distances

Authors : Ana Maria A. C. Rocha, M. Fernanda P. Costa, Edite M. G. P. Fernandes

Published in: Optimization, Learning Algorithms and Applications

Publisher: Springer International Publishing

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Abstract

This paper contains a proposal to assign points to clusters, represented by their centers, based on weighted expected distances in a cluster analysis context. The proposed clustering algorithm has mechanisms to create new clusters, to merge two nearby clusters and remove very small clusters, and to identify points ‘noise’ when they are beyond a reasonable neighborhood of a center or belong to a cluster with very few points. The presented clustering algorithm is evaluated using four randomly generated and two well-known data sets. The obtained clustering is compared to other clustering algorithms through the visualization of the clustering, the value of the DB validity measure and the value of the sum of within-cluster distances. The preliminary comparison of results shows that the proposed clustering algorithm is very efficient and effective.

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previous chapter Model-Agnostic Multi-objective Approach for the Evolutionary Discovery of Mathematical Models
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Footnotes
1
available at Mostapha Kalami Heris, Evolutionary Data Clustering in MATLAB (URL: https://​yarpiz.​com/​64/​ypml101-evolutionary-clustering), Yarpiz, 2015.
 
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Metadata
Title
A Simple Clustering Algorithm Based on Weighted Expected Distances
Authors
Ana Maria A. C. Rocha
M. Fernanda P. Costa
Edite M. G. P. Fernandes
Copyright Year
2021
Book
Optimization, Learning Algorithms and Applications

Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-91885-9_7

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