research-article

Chameleon algorithm based on mutual k-nearest neighbors

Authors:

Ling DingAuthors Info & Claims

Applied Intelligence, Volume 51, Issue 4

Pages 2031 - 2044

https://doi.org/10.1007/s10489-020-01926-7

Published: 01 April 2021 Publication History

Abstract

Clustering is a typical unsupervised data analysis method, which divides a given data set without label information into multiple clusters. The data on each cluster has a great deal of association, which can be used as the preprocessing stage of other algorithms or for further association analysis. Therefore, clustering plays an important role in a wide range of fields. Chameleon is a clustering algorithm that combines the relative interconnectivity and relative closeness to find clusters of arbitrary shape with high quality. However, the graph-partitioning technology hMETIS algorithm used in the algorithm is difficult to operate and easy to cause uncertainty of results. In addition, the final number of clusters need to be specified by user as a parameter to stop merging, which is difficult to determine without prior information. Aiming at these shortcomings, Chameleon algorithm based on mutual k-nearest neighbors (MChameleon) is proposed. Firstly, the idea of mutual k-nearest neighbors is introduced to directly generate sub-clusters, which omits the process of partitioning graph. Then, the concept of MC modularity is introduced, which is used to objectively identify the final clustering results. By experiments on artificial data sets and UCI data sets, we compared MChameleon with the original Chameleon algorithm, the improved AChameleon algorithm and the classic K-Means, DBSCAN, BIRCH algorithm in accuracy. Experimental results on data sets show that Chameleon algorithm based on mutual k-nearest neighbors has great advantages and is feasible.

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Cited By

Zhu TLi CLiu TShao Y(2024)Learning using granularity statistical invariants for classificationApplied Intelligence10.1007/s10489-024-05506-x54:8(6667-6681)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05506-x
Sorkhi MAkbari ERabbani MMotameni H(2024)A dynamic density-based clustering method based on K-nearest neighborKnowledge and Information Systems10.1007/s10115-023-02038-766:5(3005-3031)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10115-023-02038-7
Yang YQian CLi HGao YWu JLiu CZhao S(2022)An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learningThe Journal of Supercomputing10.1007/s11227-022-04634-w78:18(19566-19604)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11227-022-04634-w

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Chameleon algorithm based on mutual k-nearest neighbors

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cover image Applied Intelligence

Applied Intelligence Volume 51, Issue 4

Apr 2021

874 pages

ISSN:0924-669X

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© Springer Science+Business Media, LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2021

Accepted: 02 September 2020

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the National Natural Science Foundations of China

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Cited By

Zhu TLi CLiu TShao Y(2024)Learning using granularity statistical invariants for classificationApplied Intelligence10.1007/s10489-024-05506-x54:8(6667-6681)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05506-x
Sorkhi MAkbari ERabbani MMotameni H(2024)A dynamic density-based clustering method based on K-nearest neighborKnowledge and Information Systems10.1007/s10115-023-02038-766:5(3005-3031)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10115-023-02038-7
Yang YQian CLi HGao YWu JLiu CZhao S(2022)An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learningThe Journal of Supercomputing10.1007/s11227-022-04634-w78:18(19566-19604)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11227-022-04634-w

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