tutorial

Correlation clustering: from theory to practice

Authors:

Francesco Bonchi,

David Garcia-Soriano,

Edo LibertyAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Page 1972

https://doi.org/10.1145/2623330.2630808

Published: 24 August 2014 Publication History

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Abstract

Correlation clustering is arguably the most natural formulation of clustering. Given a set of objects and a pairwise similarity measure between them, the goal is to cluster the objects so that, to the best possible extent, similar objects are put in the same cluster and dissimilar objects are put in different clusters. As it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and in particular makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain.

Despite its simplicity, generality and wide applicability, correlation clustering has so far received much more attention from the algorithmic theory community than from the data mining community. The goal of this tutorial is to show how correlation clustering can be a powerful addition to the toolkit of the data mining researcher and practitioner, and to encourage discussions and further research in the area.

In the tutorial we will survey the problem and its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. We will motivate the problems and discuss real-world applications, the scalability issues that may arise, and the existing approaches to handle them.

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Cited By

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Anderson AMondal ABarford PCrovella MSommers J(2024)An Elemental Decomposition of DNS Name-to-IP GraphsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621147(1661-1670)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621147
Gullo FMandaglio DTagarelli A(2023)A combinatorial multi-armed bandit approach to correlation clusteringData Mining and Knowledge Discovery10.1007/s10618-023-00937-537:4(1630-1691)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s10618-023-00937-5
Cordner NKollios G(2023)An Efficient Local Search Algorithm for Correlation Clustering on Large GraphsCombinatorial Optimization and Applications10.1007/978-3-031-49611-0_1(3-15)Online publication date: 9-Dec-2023
https://doi.org/10.1007/978-3-031-49611-0_1
Show More Cited By

Index Terms

Correlation clustering: from theory to practice
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2014

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clustering

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Tutorial

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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Anderson AMondal ABarford PCrovella MSommers J(2024)An Elemental Decomposition of DNS Name-to-IP GraphsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621147(1661-1670)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621147
Gullo FMandaglio DTagarelli A(2023)A combinatorial multi-armed bandit approach to correlation clusteringData Mining and Knowledge Discovery10.1007/s10618-023-00937-537:4(1630-1691)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s10618-023-00937-5
Cordner NKollios G(2023)An Efficient Local Search Algorithm for Correlation Clustering on Large GraphsCombinatorial Optimization and Applications10.1007/978-3-031-49611-0_1(3-15)Online publication date: 9-Dec-2023
https://doi.org/10.1007/978-3-031-49611-0_1
Bonchi FGarcía-Soriano DGullo F(2022)Correlation ClusteringSynthesis Lectures on Data Mining and Knowledge Discovery10.2200/S01163ED1V01Y202201DMK01912:1(1-149)Online publication date: 8-Mar-2022
https://doi.org/10.2200/S01163ED1V01Y202201DMK019
Chang YSu HMilani AWoelfel P(2022)Narrowing the LOCAL-CONGEST Gaps in Sparse Networks via Expander DecompositionsProceedings of the 2022 ACM Symposium on Principles of Distributed Computing10.1145/3519270.3538423(301-312)Online publication date: 20-Jul-2022
https://dl.acm.org/doi/10.1145/3519270.3538423
Ahn KCormode GGuha SMcGregor AWirth A(2021)Correlation Clustering in Data StreamsAlgorithmica10.1007/s00453-021-00816-9Online publication date: 13-Mar-2021
https://doi.org/10.1007/s00453-021-00816-9
Mandaglio DTagarelli AGullo F(2021)Correlation Clustering with Global Weight BoundsMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-030-86520-7_31(499-515)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-3-030-86520-7_31
Pandove DGoel SRani R(2017)Correlation clustering methodologies and their fundamental resultsExpert Systems10.1111/exsy.1222935:1Online publication date: 13-Sep-2017
https://doi.org/10.1111/exsy.12229
Pelillo MElezi IFiorucci M(2017)Revealing structure in large graphsPattern Recognition Letters10.1016/j.patrec.2016.09.00787:C(4-11)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.patrec.2016.09.007
Anava YAvigdor-Elgrabli NGamzu IGangemi ALeonardi SPanconesi A(2015)Improved Theoretical and Practical Guarantees for Chromatic Correlation ClusteringProceedings of the 24th International Conference on World Wide Web10.1145/2736277.2741629(55-65)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2736277.2741629

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