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24-07-2020

Initializing k-means Clustering by Bootstrap and Data Depth

Authors: Aurora Torrente, Juan Romo

Published in: Journal of Classification | Issue 2/2021

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Abstract

The k-means algorithm is widely used in various research fields because of its fast convergence to the cost function minima; however, it frequently gets stuck in local optima as it is sensitive to initial conditions. This paper explores a simple, computationally feasible method, which provides k-means with a set of initial seeds to cluster datasets of arbitrary dimensions. Our technique consists of two stages: firstly, we use the original data space to obtain a set of prototypes (cluster centers) by applying k-means to bootstrap replications of the data and, secondly, we cluster the space of centers, which has tighter (thus easier to separate) groups, and search the deepest point in each assembled cluster using a depth notion. We test this method with simulated and real data, compare it with commonly used k-means initialization algorithms, and show that it is feasible and more efficient than previous proposals in many situations.
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Metadata
Title
Initializing k-means Clustering by Bootstrap and Data Depth
Authors
Aurora Torrente
Juan Romo
Publication date
24-07-2020
Publisher
Springer US
Published in
Journal of Classification / Issue 2/2021
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-020-09372-3

Other articles of this Issue 2/2021

Editorial: Journal of Classification Vol. 38-2

Model-based Clustering of Count Processes

Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals

An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints

Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data

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