article

Clusterer ensemble

Authors:

Zhi-Hua Zhou,

Wei TangAuthors Info & Claims

Knowledge-Based Systems, Volume 19, Issue 1

Pages 77 - 83

https://doi.org/10.1016/j.knosys.2005.11.003

Published: 01 March 2006 Publication History

Abstract

Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here, an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. similar clusters are assigned with the same label, by counting their overlapped data items. Then, four methods are developed to combine the aligned clusterers. Experiments show that clustering performance could be significantly improved by ensemble methods, where utilizing mutual information to select a subset of clusterers for weighted voting is a nice choice. Since the proposed methods work by analyzing the clustering results instead of the internal mechanisms of the component clusterers, they are applicable to diverse kinds of clustering algorithms.

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Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
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Zhou PDu LLiu XLing ZJi XLi XShen Y(2023)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332191336:5(2096-2109)Online publication date: 4-Oct-2023
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Clusterer ensemble
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Published In

Knowledge-Based Systems Volume 19, Issue 1

March, 2006

104 pages

ISSN:0950-7051

Issue’s Table of Contents

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2006

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Zhou PLi RLing ZDu LLiu X(2025)Fair Clustering Ensemble With Equal Cluster CapacityIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.350785747:3(1729-1746)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TPAMI.2024.3507857
Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3311409
Zhou PDu LLiu XLing ZJi XLi XShen Y(2023)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332191336:5(2096-2109)Online publication date: 4-Oct-2023
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Shi YYang KYu ZChen CZeng H(2023)Adaptive Ensemble Clustering With Boosting BLS-Based AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327112035:12(12369-12383)Online publication date: 1-Dec-2023
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Zhou PDu LLi X(2023)Adaptive Consensus Clustering for Multiple K-Means Via Base Results RefiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326497035:10(10251-10264)Online publication date: 1-Oct-2023
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Wu DLi JMo XWu W(2023)DFECTS: A Deep Fuzzy Ensemble Clusterer for Time SeriesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0834-5_5(61-80)Online publication date: 20-Oct-2023
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Sun YWang SHuang DSun YHu ASun J(2022)A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shapedIntelligent Data Analysis10.3233/IDA-21611226:5(1211-1228)Online publication date: 1-Jan-2022
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Du HWang WBai LFeng J(2022)A Generative Clustering Ensemble Model and Its Application in IoT Data AnalysisWireless Communications & Mobile Computing10.1155/2022/80811772022Online publication date: 1-Jan-2022
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Zhou PLiu XDu LLi X(2022)Self-paced Adaptive Bipartite Graph Learning for Consensus ClusteringACM Transactions on Knowledge Discovery from Data10.1145/356470117:5(1-35)Online publication date: 27-Sep-2022
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Xu LDing S(2021)A novel clustering ensemble model based on granular computingApplied Intelligence10.1007/s10489-020-01979-851:8(5474-5488)Online publication date: 1-Aug-2021
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