Adaptive Initialization Method for K-Means Algorithm

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering perform...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jie Yang, Yu-Kai Wang, Xin Yao, Chin-Teng Lin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	k-means adaptive initialization method initial cluster centers clustering Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6bbbbdc684c34de5be854a7debfdb9da
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6bbbbdc684c34de5be854a7debfdb9da
record_format	dspace
spelling	oai:doaj.org-article:6bbbbdc684c34de5be854a7debfdb9da2021-12-01T02:32:26ZAdaptive Initialization Method for K-Means Algorithm2624-821210.3389/frai.2021.740817https://doaj.org/article/6bbbbdc684c34de5be854a7debfdb9da2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frai.2021.740817/fullhttps://doaj.org/toc/2624-8212The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.Jie YangYu-Kai WangXin YaoXin YaoChin-Teng LinFrontiers Media S.A.articlek-meansadaptiveinitialization methodinitial cluster centersclusteringElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Artificial Intelligence, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	k-means adaptive initialization method initial cluster centers clustering Electronic computers. Computer science QA75.5-76.95
spellingShingle	k-means adaptive initialization method initial cluster centers clustering Electronic computers. Computer science QA75.5-76.95 Jie Yang Yu-Kai Wang Xin Yao Xin Yao Chin-Teng Lin Adaptive Initialization Method for K-Means Algorithm
description	The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.
format	article
author	Jie Yang Yu-Kai Wang Xin Yao Xin Yao Chin-Teng Lin
author_facet	Jie Yang Yu-Kai Wang Xin Yao Xin Yao Chin-Teng Lin
author_sort	Jie Yang
title	Adaptive Initialization Method for K-Means Algorithm
title_short	Adaptive Initialization Method for K-Means Algorithm
title_full	Adaptive Initialization Method for K-Means Algorithm
title_fullStr	Adaptive Initialization Method for K-Means Algorithm
title_full_unstemmed	Adaptive Initialization Method for K-Means Algorithm
title_sort	adaptive initialization method for k-means algorithm
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/6bbbbdc684c34de5be854a7debfdb9da
work_keys_str_mv	AT jieyang adaptiveinitializationmethodforkmeansalgorithm AT yukaiwang adaptiveinitializationmethodforkmeansalgorithm AT xinyao adaptiveinitializationmethodforkmeansalgorithm AT xinyao adaptiveinitializationmethodforkmeansalgorithm AT chintenglin adaptiveinitializationmethodforkmeansalgorithm
_version_	1718405908124401664

Adaptive Initialization Method for K-Means Algorithm

Ejemplares similares