k-means: A revisit

Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. The performance of k-means has been enhanced from different perspectives over the years. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-...

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Main Authors: ZHAO, Wan-Lei, DENG, Cheng-Hao, NGO, Chong-wah
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/6304
https://ink.library.smu.edu.sg/context/sis_research/article/7307/viewcontent/kmean.pdf
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spelling sg-smu-ink.sis_research-73072021-11-23T06:59:49Z k-means: A revisit ZHAO, Wan-Lei DENG, Cheng-Hao NGO, Chong-wah Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. The performance of k-means has been enhanced from different perspectives over the years. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-means variant is presented. Different from most of k-means variants, the clustering procedure is driven by an explicit objective function, which is feasible for the whole l(2)-space. The classic egg-chicken loop in k-means has been simplified to a pure stochastic optimization procedure. The procedure of k-means becomes simpler and converges to a considerably better local optima. The effectiveness of this new variant has been studied extensively in different contexts, such as document clustering, nearest neighbor search and image clustering. Superior performance is observed across different scenarios. (c) 2018 Elsevier B.V. All rights reserved. 2018-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6304 info:doi/10.1016/j.neucom.2018.02.072 https://ink.library.smu.edu.sg/context/sis_research/article/7307/viewcontent/kmean.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Clustering k-means Incremental optimization Computer Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Clustering
k-means
Incremental optimization
Computer Engineering
spellingShingle Clustering
k-means
Incremental optimization
Computer Engineering
ZHAO, Wan-Lei
DENG, Cheng-Hao
NGO, Chong-wah
k-means: A revisit
description Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. The performance of k-means has been enhanced from different perspectives over the years. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-means variant is presented. Different from most of k-means variants, the clustering procedure is driven by an explicit objective function, which is feasible for the whole l(2)-space. The classic egg-chicken loop in k-means has been simplified to a pure stochastic optimization procedure. The procedure of k-means becomes simpler and converges to a considerably better local optima. The effectiveness of this new variant has been studied extensively in different contexts, such as document clustering, nearest neighbor search and image clustering. Superior performance is observed across different scenarios. (c) 2018 Elsevier B.V. All rights reserved.
format text
author ZHAO, Wan-Lei
DENG, Cheng-Hao
NGO, Chong-wah
author_facet ZHAO, Wan-Lei
DENG, Cheng-Hao
NGO, Chong-wah
author_sort ZHAO, Wan-Lei
title k-means: A revisit
title_short k-means: A revisit
title_full k-means: A revisit
title_fullStr k-means: A revisit
title_full_unstemmed k-means: A revisit
title_sort k-means: a revisit
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/6304
https://ink.library.smu.edu.sg/context/sis_research/article/7307/viewcontent/kmean.pdf
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