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Open Access

Approximation Algorithm for the Balanced 2-Correlation Clustering Problem

Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, China
Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, China
Faculty of Business Administration, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
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Abstract

The Correlation Clustering Problem (CorCP) is a significant clustering problem based on the similarity of data. It has significant applications in different fields, such as machine learning, biology, and data mining, and many different problems in other areas. In this paper, the Balanced 2-CorCP (B 2-CorCP) is introduced and examined, and a new interesting variant of the CorCP is described. The goal of this clustering problem is to partition the vertex set into two clusters with equal size, such that the number of disagreements is minimized. We first present a polynomial time algorithm for the B 2-CorCP on M-positive edge dominant graphs (M⩾3). Then, we provide a series of numerical experiments, and the results show the effectiveness of our algorithm.

Keywords

balanced clusteringk-correlation clusteringpositive edge dominant graphsapproximation algorithm

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Tsinghua Science and Technology
Volume 27 Issue 5,
October 2022
Pages 777-784
DOI: 10.26599/TST.2021.9010051
Cite this article:
Ji S, Xu D, Du D, et al. Approximation Algorithm for the Balanced 2-Correlation Clustering Problem. Tsinghua Science and Technology, 2022, 27(5): 777-784. https://doi.org/10.26599/TST.2021.9010051

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Received: 13 February 2021
Revised: 27 May 2021
Accepted: 28 June 2021
Published: 17 March 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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