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A Literature Review on Correlation Clustering: Cross-disciplinary Taxonomy with Bibliometric Analysis

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Abstract

The correlation clustering problem identifies clusters in a set of objects when the qualitative information about objects’ mutual similarities or dissimilarities is given in a signed network. This clustering problem has been studied in different scientific areas, including computer sciences, operations research, and social sciences. A plethora of applications, problem extensions, and solution approaches have resulted from these studies. This paper focuses on the cross-disciplinary evolution of this problem by analysing the taxonomic and bibliometric developments during the 1992 to 2020 period. With the aim of enhancing cross-fertilization of knowledge, we present a unified discussion of the problem, including details of several mathematical formulations and solution approaches. Additionally, we analyse the literature gaps and propose some dominant research directions for possible future studies.

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References

  1. Nelson K (1973) Some evidence for the cognitive primacy of categorization and its functional basis. Merrill-Palmer Quarterly of Behavior and Development 19(1):21–39

    Google Scholar 

  2. Rosch E (1977) Classification of real-world objects: Origins and representations in cognition. Thinking: Readings in Cognitive Science, p 212–222

  3. Bonchi F, García-Soriano D, Gullo F (2022) Correlation clustering. Synthesis Lectures on Data Mining and Knowledge Discovery 12(1):1–149

    Article  Google Scholar 

  4. Wirth A (2010) Correlation Clustering. Springer US, Boston, MA, p 227–231

  5. Cohen WW, Richman J (2002) Learning to match and cluster large high-dimensional data sets for data integration. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p 475–480

  6. Néda Z, Florian R, Ravasz M, Libál A, Györgyi G (2006) Phase transition in an optimal clusterization model. Phys A 362(2):357–368

    Article  Google Scholar 

  7. Néda Z, Sumi R, Ercsey-Ravasz M, Varga M, Molnár B, Cseh G (2009) Correlation clustering on networks. J Phys A: Math Theor 42(34):345003

  8. Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data (TKDD) 1(1):4–es

  9. Cesa-Bianchi N, Gentile C, Vitale F, Zappella G (2012) A correlation clustering approach to link classification in signed networks. In: Conference on Learning Theory. JMLR Workshop and Conference Proceedings, p 34–1

  10. Il’ev V, Il’eva S, Kononov A (2016) Short survey on graph correlation clustering with minimization criteria. In: International Conference on Discrete Optimization and Operations Research. Springer, p 25–36

  11. Pandove D, Goel S, Rani R (2018) Correlation clustering methodologies and their fundamental results. Expert Syst 35(1):e12229

  12. Doreian P, Mrvar A (2009) Partitioning signed social networks. Soc Networks 31(1):1–11

    Article  Google Scholar 

  13. Figueiredo R, Moura G (2013) Mixed integer programming formulations for clustering problems related to structural balance. Social Networks 35(4):639–651

    Article  Google Scholar 

  14. Levorato M, Drummond L, Frota Y, Figueiredo R (2015) An ILS algorithm to evaluate structural balance in signed social networks. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing. p 1117–1122

  15. Bair E (2013) Semi-supervised clustering methods. Wiley Interdiscip Rev Comput Stat 5(5):349–361

    Article  Google Scholar 

  16. Chunaev P (2020) Community detection in node-attributed social networks: a survey. Computer Science Review 37:100286

  17. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174

    Article  Google Scholar 

  18. Grira N, Crucianu M, Boujemaa N (2004) Unsupervised and semi-supervised clustering: a brief survey. A review of machine learning techniques for processing multimedia content. 1:9–16

  19. Harenberg S, Bello G, Gjeltema L, Ranshous S, Harlalka J, Seay R, Padmanabhan K, Samatova N (2014) Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdisciplinary Reviews: Computational Statistics 6(6):426–439

    Article  Google Scholar 

  20. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  21. Kim W (2009) Parallel clustering algorithms: survey. Parallel Algorithms, Spring 34:43

    Google Scholar 

  22. Nguyen HL, Woon YK, Ng WK (2015) A survey on data stream clustering and classification. Knowl Inf Syst 45(3):535–569

    Article  Google Scholar 

  23. Rokach L (2009) A survey of clustering algorithms. In: Data Mining and Knowledge Discovery Handbook. Springer, p 269–298

  24. Schaeffer SE (2007) Graph clustering. Computer Science Review 1(1):27–64

    Article  Google Scholar 

  25. Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Annals of Data Science 2(2):165–193

    Article  Google Scholar 

  26. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Networks 16(3):645–678

    Article  Google Scholar 

  27. Heider F (1946) Attitudes and cognitive organization. J Psychol 21(1):107–112

    Article  Google Scholar 

  28. Cartwright D, Harary F (1956) Structural balance: a generalization of heider’s theory. Psychol Rev 63(5):277

    Article  Google Scholar 

  29. Davis JA (1967) Clustering and structural balance in graphs. Hum Relat 20(2):181–187

    Article  Google Scholar 

  30. Doreian P, Mrvar A (1996) A partitioning approach to structural balance. Soc Networks 18(2):149–168

    Article  Google Scholar 

  31. Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6(3–4):281–297

    Article  Google Scholar 

  32. Chen ZZ, Jiang T, Lin GH (2001) Computing phylogenetic roots with bounded degrees and errors. In: Workshop on Algorithms and Data Structures. Springer, p 377–388

  33. Shamir R, Sharan R, Tsur D (2004) Cluster graph modification problems. Discret Appl Math 144(1–2):173–182

    Article  Google Scholar 

  34. Zahn CT Jr (1964) Approximating symmetric relations by equivalence relations. J Soc Ind Appl Math 12(4):840–847

    Article  Google Scholar 

  35. Régnier S (1965) On some mathematical aspects of automatics classification problems. ICC Bulletin 4(3):175

    Google Scholar 

  36. Ambrosi K (1984) Aggregation binärer relationen in der qualitativen datenanalyse

  37. Barthelemy JP, Monjardet B (1981) The median procedure in cluster analysis and social choice theory. Math Soc Sci 1(3):235–267

    Article  Google Scholar 

  38. Marcotorchino J, Michaud P (1981) Heuristic approach of the similarity aggregation problem. Methods of Operations Research 43:395–404

    Google Scholar 

  39. Marcotorchino J, Michaud P (1981) Optimization in exploratory data analysis. In: Proceedings of 5th International Symposium on Operations Research. Physica Verlag Köln

  40. Mirkin B (1974) The problems of approximation in space of relations and qualitative data analysis. Information and Remote Control 35(1424–1431):2

    Google Scholar 

  41. Opitz O, Schader M (1984) Analyse qualitativer daten: einführung und übersicht. Operations-Research-Spektrum 6(2):67–83

  42. Doyle JR (1992) MCC–multiple correlation clustering. Int J Man Mach Stud 37(6):751–765

  43. Bansal N, Blum A, Chawla S (2004) Correlation clustering. Mach Learn 56(1):89–113

    Article  Google Scholar 

  44. Garey MR, Johnson DS (1979) Computers and intractability, vol 174. Freeman San Francisco

  45. Avidor A, Langberg M (2007) The multi-multiway cut problem. Theoret Comput Sci 377(1–3):35–42

    Article  Google Scholar 

  46. Charikar M, Guruswami V, Wirth A (2005) Clustering with qualitative information. J Comput Syst Sci 71(3):360–383

    Article  Google Scholar 

  47. Demaine ED, Emanuel D, Fiat A, Immorlica N (2006) Correlation clustering in general weighted graphs. Theoret Comput Sci 361(2–3):172–187

    Article  Google Scholar 

  48. Ailon N, Charikar M, Newman A (2008) Aggregating inconsistent information: ranking and clustering. J ACM (JACM) 55(5):1–27

    Google Scholar 

  49. Demaine ED, Immorlica N (2003) Correlation clustering with partial information. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer, Berlin, Heidelberg, p 1–13

  50. Wirth AI (2005) Approximation algorithms for clustering. Princeton University

  51. Wahid DF (2017) Random models and heuristic algorithms for correlation clustering problems on signed social networks. Ph.D. thesis, University of British Columbia

  52. Chawla S, Makarychev K, Schramm T, Yaroslavtsev G (2015) Near optimal LP rounding algorithm for correlationclustering on complete and complete k-partite graphs. In: Proceedings of the forty-seventh annual ACM symposium on Theory of computing. p 219–228

  53. Charikar M, Wirth A (2004) Maximizing quadratic programs: Extending Grothendieck’s inequality. In: 45th Annual IEEE Symposium on Foundations of Computer Science. IEEE, p 54–60

  54. Swamy C (2004) Correlation clustering: maximizing agreements via semidefinite programming. In: SODA, vol 4. Citeseer, pp 526–527

  55. Abdelnasser A, Hossain E, Kim DI (2014) Clustering and resource allocation for dense femtocells in a two-tier cellular OFDMA network. IEEE Trans Wireless Commun 13(3):1628–1641

    Article  Google Scholar 

  56. Ahn K, Cormode G, Guha S, McGregor A, Wirth A (2015) Correlation clustering in data streams. In: International Conference on Machine Learning. PMLR, p 2237–2246

  57. Giotis I, Guruswami V (2006) Correlation clustering with a fixed number of clusters. In: Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm. p 1167–1176

  58. Mathieu C, Schudy W (2010) Correlation clustering with noisy input. In: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, p 712–728

  59. Zaw CW, Tun YK, Hong CS (2017) User clustering based on correlation in 5G using semidefinite programming. In: 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, p 342–345

  60. Samal M, Saradhi VV, Nandi S (2018) Scalability of correlation clustering. Pattern Anal Appl 21(3):703–719

    Article  Google Scholar 

  61. Bonizzoni P, Della Vedova G, Dondi R, Jiang T (2008) On the approximation of correlation clustering and consensus clustering. J Comput Syst Sci 74(5):671–696

    Article  Google Scholar 

  62. Lorrain F, White HC (1971) Structural equivalence of individuals in social networks. J Math Sociol 1(1):49–80

    Article  Google Scholar 

  63. Batagelj V (1997) Notes on blockmodeling. Soc Networks 19(2):143–155

    Article  Google Scholar 

  64. Doreian P, Krackhardt D (2001) Pre-transitive balance mechanisms for signed networks. J Math Sociol 25(1):43–67

    Article  Google Scholar 

  65. De Nooy W, Mrvar A, Batagelj V (2018) Exploratory social network analysis with Pajek: revised and expanded edition for updated software, vol 46. Cambridge University Press

  66. Doreian P (2008) A multiple indicator approach to blockmodeling signed networks. Soc Networks 30(3):247–258

    Article  Google Scholar 

  67. Drummond L, Figueiredo R, Frota Y, Levorato M (2013) Efficient solution of the correlation clustering problem: an application to structural balance. In: OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. Springer, p 674–683

  68. Figueiredo R, Frota Y (2014) The maximum balanced subgraph of a signed graph: applications and solution approaches. Eur J Oper Res 236(2):473–487

    Article  Google Scholar 

  69. Williamson DP, Shmoys DB (2011) The design of approximation algorithms. Cambridge University Press

  70. Coleman T, Saunderson J, Wirth A (2008) A local-search 2-approximation for 2-correlation-clustering. In: European Symposium on Algorithms. Springer, p 308–319

  71. Ailon N, Avigdor-Elgrabli N, Liberty E, Van Zuylen A (2012) Improved approximation algorithms for bipartite correlation clustering. SIAM J Comput 41(5):1110–1121

    Article  Google Scholar 

  72. Fukunaga T (2019) Lp-based pivoting algorithm for higher-order correlation clustering. J Comb Optim 37(4):1312–1326

    Article  Google Scholar 

  73. Cambus M, Choo D, Miikonen H, Uitto J (2021) Massively parallel correlation clustering in bounded arboricity graphs. arXiv preprint arXiv:2102.11660

  74. Chierichetti F, Dalvi N, Kumar R (2014) Correlation clustering in mapreduce. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p 641–650

  75. Pan X, Papailiopoulos D, Oymak S, Recht B, Ramchandran K, Jordan MI (2015) Parallel correlation clustering on big graphs. Adv Neural Inf Proces Syst 28

  76. Javed MA, Younis MS, Latif S, Qadir J, Baig A (2018) Community detection in networks: a multidisciplinary review. J Netw Comput Appl 108:87–111

    Article  Google Scholar 

  77. Achtert E, Böhm C, Kriegel HP, Kröger P, Zimek A (2007) Robust, complete, and efficient correlation clustering. In: Proceedings of the 2007 SIAM International Conference on Data Mining. SIAM, p 413–418

  78. Zhang Z, Cheng H, Chen W, Zhang S, Fang Q (2008) Correlation clustering based on genetic algorithm for documents clustering. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, p 3193–3198

  79. Wang N, Li J (2013) Restoring: a greedy heuristic approach based on neighborhood for correlation clustering. In: International Conference on Advanced Data Mining and Applications. Springer, p 348–359

  80. Lingas A, Persson M, Sledneu D (2014) Iterative merging heuristics for correlation clustering. International Journal of Metaheuristics 3(2):105–117

    Article  Google Scholar 

  81. Levorato M, Figueiredo R, Frota Y, Drummond L (2017) Evaluating balancing on social networks through the efficient solution of correlation clustering problems. EURO Journal on Computational Optimization 5(4):467–498

    Article  Google Scholar 

  82. Veldt N, Gleich DF, Wirth A, Saunderson J (2019) Metric-constrained optimization for graph clustering algorithms. SIAM Journal on Mathematics of Data Science 1(2):333–355

    Article  Google Scholar 

  83. Aszalcós L, Bakó M (2017) Correlation clustering: a parallel approach? In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, p 403–406

  84. Keuper M, Lukasik J, Singh M, Yarkony J (2019) Massively parallel benders decomposition for correlation clustering. arXiv preprint arXiv:1902.05659

  85. Ji S, Xu D, Du D, Gai L (2020) Approximation algorithm for the balanced 2-correlation clustering problem on well-proportional graphs. In: International Conference on Algorithmic Applications in Management. Springer, p 97–107

  86. Ailon N, Bhattacharya A, Jaiswal R (2018) Approximate correlation clustering using same-cluster queries. In: Latin American Symposium on Theoretical Informatics. Springer, Cham, p 14–27

  87. Klein PN, Mathieu C, Zhou H (2015) Correlation clustering and two-edge-connected augmentation for planar graphs. In: 32nd International Symposium on Theoretical Aspects of Computer Science (STACS 2015). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik

  88. Rebagliati N, RotaBulò S, Pelillo M (2013) Correlation clustering with stochastic labellings. In: International Workshop on Similarity-Based Pattern Recognition. Springer, Berlin, Heidelberg, p 120–133

  89. Makarychev K, Makarychev Y, Vijayaraghavan A (2015) Correlation clustering with noisy partial information. In: Conference on Learning Theory. PMLR, p 1321–1342

  90. Puleo GJ, Milenkovic O (2015) Correlation clustering with constrained cluster sizes and extended weights bounds. SIAM J Optim 25(3):1857–1872

    Article  Google Scholar 

  91. Veldt N, Wirth AI, Gleich DF (2017) Correlation clustering with low-rank matrices. In: Proceedings of the 26th International Conference on World Wide Web. p 1025–1034

  92. Gleich DF, Veldt N, Wirth A (2018) Correlation clustering generalized. arXiv preprint arXiv:1809.0949

  93. Li P, Dau H, Puleo G, Milenkovic O (2017) Motif clustering and overlapping clustering for social network analysis. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, p 1–9

  94. Hua J, Yu J, Yang MS (2021) Star-based learning correlation clustering. Pattern Recogn 116:107966

  95. Geerts F, Ndindi R (2016) Bounded correlation clustering. International Journal of Data Science and Analytics 1(1):17–35

    Article  Google Scholar 

  96. Bonchi F, Gionis A, Ukkonen A (2013) Overlapping correlation clustering. Knowl Inf Syst 35(1):1–32

    Article  Google Scholar 

  97. Andrade CE, Resende MG, Karloff HJ, Miyazawa FK (2014) Evolutionary algorithms for overlapping correlation clustering. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. p 405–412

  98. Chagas GO, Lorena LAN, dos Santos RDC (2019) A hybrid heuristic for the overlapping cluster editing problem. Appl Soft Comput 81:105482

  99. Bonchi F, Gionis A, Gullo F, Tsourakakis CE, Ukkonen A (2015) Chromatic correlation clustering. ACM Trans Knowl Discov Data (TKDD) 9(4):1–24

    Article  Google Scholar 

  100. Hmimida M, Kanawati R (2015) Community detection in multiplex networks: a seed-centric approach. Networks & Heterogeneous Media 10(1):71

    Article  Google Scholar 

  101. Huang Y, Wang H (2016) Consensus and multiplex approach for community detection in attributed networks. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, p 425–429

  102. Lancichinetti A, Fortunato S (2012) Consensus clustering in complex networks. Sci Rep 2(1):1–7

    Article  Google Scholar 

  103. Mondragon RJ, Iacovacci J, Bianconi G (2018) Multilink communities of multiplex networks. PLoS ONE 13(3):e0193821

  104. Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26(4):354–359

    Article  Google Scholar 

  105. Roy A, Pokutta S (2017) Hierarchical clustering via spreading metrics. J Mach Learn Res 18:1–35

    Google Scholar 

  106. Bhattacharya A, De RK (2008) Divisive correlation clustering algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles. Bioinformatics 24(11):1359–1366

  107. Sumi R, Neda Z (2008) Molecular dynamics approach to correlation clustering. Int J Mod Phys C 19(09):1349–1358

    Article  Google Scholar 

  108. Wei F, Sakata K, Asakura T, Kikuchi J et al (2018) Systemic homeostasis in metabolome, ionome, and microbiome of wild Yellowfin Goby in Estuarine ecosystem. Sci Rep 8(1):1–12

    Google Scholar 

  109. Akorli J, Namaali PA, Ametsi GW, Egyirifa RK, Pels NAP (2019) Generational conservation of composition and diversity of field-acquired midgut microbiota in anopheles gambiae (sensu lato) during colonization in the laboratory. Parasit Vectors 12(1):1–9

    Article  Google Scholar 

  110. Bakó M (2018) The efficiency of classification in imperfect databases: comparing KNN and correlation clustering. In: Annales Mathematicae et Informaticae, vol 49. Eszterházy Károly Egyetem Líceum Kiadó, pp 11–20

  111. Barik S, Das S, Vikalo H (2018) Qsdpr: Viral quasispecies reconstruction via correlation clustering. Genomics 110(6):375–381

    Article  Google Scholar 

  112. Belyaeva A, Venkatachalapathy S, Nagarajan M, Shivashankar G, Uhler C (2017) Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription. Proc Natl Acad Sci 114(52):13714–13719

    Article  Google Scholar 

  113. Bessonov K, Walkey CJ, Shelp BJ, van Vuuren HJ, Chiu D, van der Merwe G (2013) Functional analyses of NSF1 in wine yeast using interconnected correlation clustering and molecular analyses. PLoS ONE 8(10):e77192

  114. Bhattacharya A, De RK (2010) Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values. J Biomed Inform 43(4):560–568

    Article  Google Scholar 

  115. Joglekar SR (2014) Two-stage stock portfolio construction: correlation clustering and genetic optimization. In: The Twenty-Seventh International Flairs Conference

  116. Krasowski N, Beier T, Knott G, Köthe U, Hamprecht FA, Kreshuk A (2017) Neuron segmentation with high-level biological priors. IEEE Trans Med Imaging 37(4):829–839

    Article  Google Scholar 

  117. Vassy Z, Kosa I, Vassanyi I (2017) Correlation clustering of stable angina clinical care patterns for 506 thousand patients. Journal of Healthcare Engineering 2017

  118. Zhang C, Yarkony J, Hamprecht FA (2014) Cell detection and segmentation using correlation clustering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, p 9–16

  119. Alush A, Goldberger J (2012) Ensemble segmentation using efficient integer linear programming. IEEE Trans Pattern Anal Mach Intell 34(10):1966–1977

    Article  Google Scholar 

  120. Firman M, Thomas D, Julier S, Sugimoto A (2013) Learning to discover objects in RGB-D images using correlation clustering. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, p 1107–1112

  121. Kappes JH, Speth M, Reinelt G, Schnörr C (2016) Higher-order segmentation via multicuts. Comput Vis Image Underst 143:104–119

    Article  Google Scholar 

  122. Kim S, Nowozin S, Kohli P, Yoo CD (2012) Task-specific image partitioning. IEEE Trans Image Process 22(2):488–500

    Article  Google Scholar 

  123. López-Sastre RJ, Tuytelaars T, Acevedo-Rodríguez FJ, Maldonado-Bascón S (2011) Towards a more discriminative and semantic visual vocabulary. Comput Vis Image Underst 115(3):415–425

    Article  Google Scholar 

  124. Marra F, Poggi G, Sansone C, Verdoliva L (2016) Correlation clustering for PRNU-based blind image source identification. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, p 1–6

  125. Marra F, Poggi G, Sansone C, Verdoliva L (2017) Blind prnu-based image clustering for source identification. IEEE Trans Inf Forensics Secur 12(9):2197–2211

    Article  Google Scholar 

  126. Mehta A, Ashapure A, Dikshit O (2016) Segmentation-based classification of hyperspectral imagery using projected and correlation clustering techniques. Geocarto Int 31(10):1045–1057

    Article  Google Scholar 

  127. Solera F, Calderara S (2013) Social groups detection in crowd through shape-augmented structured learning. In: International Conference on Image Analysis and Processing. Springer, p 542–551

  128. Yarkony J, Ihler A, Fowlkes CC (2012) Fast planar correlation clustering for image segmentation. In: European Conference on Computer Vision. Springer, p 568–581

  129. Zhu Z, Cao G (2011) Toward privacy preserving and collusion resistance in a location proof updating system. IEEE Trans Mob Comput 12(1):51–64

    Article  Google Scholar 

  130. Aszalós L, Mihálydeák T (2015) Correlation clustering by contraction. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, p 425–434

  131. Slaoui SC, Dafir Z, Lamari Y (2018) E-transitive: an enhanced version of the transitive heuristic for clustering categorical data. Procedia Computer Science 127:26–34

    Article  Google Scholar 

  132. Dong Xie X, Zou J, Huang X (2015) Optimization for massive data query method in database. In: 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press

  133. Zhao Q, Xiong C, Yu C, Zhang C, Zhao X (2016) A new energy-aware task scheduling method for data-intensive applications in the cloud. J Netw Comput Appl 59:14–27

    Article  Google Scholar 

  134. Zhao Q, Xiong C, Zhang K, Yue Y, Yang J (2016) A data placement algorithm for data intensive applications in cloud. International Journal of Grid and Distributed Computing 9(2):145–156

    Article  Google Scholar 

  135. Albin T, Drews P, Heßeler F, Ivanescu AM, Seidl T, Abel D (2011) A hybrid control approach for low temperature combustion engine control. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference. IEEE, p 6846–6851

  136. Papenhausen E, Wang B, Ha S, Zelenyuk A, Imre D, Mueller K (2013) GPU-accelerated incremental correlation clustering of large data with visual feedback. In: 2013 IEEE International Conference on Big Data. IEEE, p 63–70

  137. Wang H, Tan SXD, Swarup S, Liu XX (2013) A power-driven thermal sensor placement algorithm for dynamic thermal management. In: 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, p 1215–1220

  138. Maatouk A, Hajri SE, Assaad M, Sari H (2018) On optimal scheduling for joint spatial division and multiplexing approach in fdd massive mimo. IEEE Trans Signal Process 67(4):1006–1021

    Article  Google Scholar 

  139. Nga NTT, Khanh NK, Hong SN (2016) Entropy-based correlation clustering for wireless sensor networks in multi-correlated regional environments. IEIE Transactions on Smart Processing and Computing 5(2):85–93

    Article  Google Scholar 

  140. Galagedera DU (2013) A new perspective of equity market performance. J Int Finan Markets Inst Money 26:333–357

    Article  Google Scholar 

  141. Isogai T (2014) Clustering of Japanese stock returns by recursive modularity optimization for efficient portfolio diversification. J Complex Networks 2(4):557–584

    Article  Google Scholar 

  142. Zhan HCJ, Rea W, Rea A (2015) An application of correlation clustering to portfolio diversification. arXiv preprint arXiv:1511.07945

  143. Mimno D, McCallum A, Mann GS (2006) Bibliometric impact measures leveraging topic analysis. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital libraries (JCDL’06). IEEE, p 65–74

  144. Morris S, DeYong C, Wu Z, Salman S, Yemenu D (2002) Diva: a visualization system for exploring document databases for technology forecasting. Comput Ind Eng 43(4):841–862

    Article  Google Scholar 

  145. Mogee ME (1991) Using patent data for technology analysis and planning. Res Technol Manag 34(4):43–49

    Article  Google Scholar 

  146. Daim TU, Rueda G, Martin H, Gerdsri P (2006) Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technol Forecast Soc Chang 73(8):981–1012

    Article  Google Scholar 

  147. Wahid DF, Ezzeldin M, Hassini E, El-Dakhakhni WW (2022) Common-knowledge networks for university strategic research planning. Decision Analytics Journal 2:100027

  148. Hu X, Leydesdorff L, Rousseau R (2020) Exponential growth in the number of items in the wos. ISSI Newsletter 16(2):32–38

    Google Scholar 

  149. Zimek A (2009) Correlation clustering. ACM SIGKDD Explorations Newsletter 11(1):53–54

    Article  Google Scholar 

  150. Aria M, Cuccurullo C (2017) bibliometrix: An r-tool for comprehensive science mapping analysis. J Informet 11(4):959–975

    Article  Google Scholar 

  151. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 3. pp 361–362

  152. Hsu JW, Huang DW (2011) Correlation between impact and collaboration. Scientometrics 86(2)317–324

  153. Abramo G, D’Angelo CA, Di Costa F (2019) The collaboration behavior of top scientists. Scientometrics 118(1):215–232

    Article  Google Scholar 

  154. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1–7):107–117

    Article  Google Scholar 

  155. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):P10008

    Article  Google Scholar 

  156. Garfield E (2004) Historiographic mapping of knowledge domains literature. J Inf Sci 30(2):119–145

    Article  Google Scholar 

  157. Klavans R, Boyack KW (2017) Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? J Am Soc Inf Sci 68(4):984–998

    Google Scholar 

  158. Esmailian P, Abtahi SE, Jalili M (2014) Mesoscopic analysis of online social networks: the role of negative ties. Phys Rev E 90(4):042817

  159. Garfield E (1990) Keywords Plus-ISI’s breakthrough retrieval method. 1. Expanding your searching power on current-contents on diskette. Current Contents 32:5–9

  160. Garfield E, Sher IH (1993) Key words plus [tm]-algorithmic derivative indexing. Journal-American Society For Information Science 44:298–298

    Article  Google Scholar 

  161. Zhang J, Yu Q, Zheng F, Long C, Lu Z, Duan Z (2016) Comparing keywords plus of WOS and author keywords: a case study of patient adherence research. J Am Soc Inf Sci 67(4):967–972

    Google Scholar 

  162. González-Álvarez J, Cervera-Crespo T (2017) Research production in high-impact journals of contemporary neuroscience: a gender analysis. J Informet 11(1):232–243

    Article  Google Scholar 

  163. Khasseh AA, Soheili F, Moghaddam HS, Chelak AM (2017) Intellectual structure of knowledge in Imetrics: a co-word analysis. Inf Process Manag 53(3):705–720

    Article  Google Scholar 

  164. Rigolon A, Browning MH, Lee K, Shin S (2018) Access to urban green space in cities of the global south: a systematic literature review. Urban Sci 2(3):67

    Article  Google Scholar 

  165. Zhao W, Mao J, Lu K (2018) Ranking themes on co-word networks: exploring the relationships among different metrics. Inf Process Manag 54(2):203–218

    Article  Google Scholar 

  166. Pan X, Papailiopoulos D, Recht B, Ramchandran K, Jordan MI (2014) Scaling up correlation clustering through parallelism and concurrency control. In: DISCML Workshop at International Conference on Neural Information Processing Systems

  167. Ben-David S, Long PM, Mansour Y (2001) Agnostic boosting. In: International Conference on Computational Learning Theory. Springer, p 507–516

  168. Kearns MJ, Schapire RE, Sellie LM (1994) Toward efficient agnostic learning. Mach Learn 17(2):115–141

    Article  Google Scholar 

  169. Pozzi S, Zoppis I, Mauri G (2005) Combinatorial and machine learning approaches in clustering microarray data. In: Biological and Artificial Intelligence Environments. Springer, p 63–71

  170. Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2(Dec):125–137

  171. Suthaharan S (2016) Support vector machine. In: Machine learning models and algorithms for big data classification. Springer, p 207–235

  172. Basu S, Banerjee A, Mooney R (2002) Semi-supervised clustering by seeding. In: In Proceedings of 19th International Conference on Machine Learning (ICML-2002). Citeseer

  173. Böcker S, Baumbach J (2013) Cluster editing. In: Conference on Computability in Europe. Springer, p 33–44

  174. Cohn D, Caruana R, McCallum A (2003) Semi-supervised clustering with user feedback. Constrained Clustering: Advances in Algorithms, Theory, and Applications 4(1):17–32

    Google Scholar 

  175. Donath WE, Hoffman AJ (2003) Lower bounds for the partitioning of graphs. In: Selected Papers of Alan J Hoffman: With Commentary. World Scientific, p 437–442

  176. Ester M, Kriegel HP, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol 96. pp 226–231

  177. Flake GW, Lawrence S, Giles CL (2000) Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p 150–160

  178. Fortunato S, Latora V, Marchiori M (2004) Method to find community structures based on information centrality. Phys Rev E 70(5):056104

  179. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  Google Scholar 

  180. Hinneburg A, Keim DA et al (1998) An efficient approach to clustering in large multimedia databases with noise. In: KDD, vol 98. pp 58–65

  181. von Luxburg U (2006) A tutorial on spectral clustering (tech. rep. 149). Max Planck Institute for Biological Cybernetics

  182. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol 1. Oakland, CA, USA, pp 281–297

  183. Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: International symposium on computer and information sciences. Springer, p 284–293

  184. Vragović I, Louis E (2006) Network community structure and loop coefficient method. Phys Rev E 74(1):016105

  185. Xu X, Yuruk N, Feng Z, Schweiger TA (2007) Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p 824–833

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Funding

This work received support from Natural Sciences and Engineering Research Council (NSERC) Discovery (Award Number: RGPIN-2020-06792) and Mitacs Accelerate Fellowship (Award Number: IT16025) programs.

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  1. School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada

    Dewan F. Wahid & Elkafi Hassini

  2. DeGroote School of Business, McMaster University, Hamilton, ON, Canada

    Elkafi Hassini

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Wahid, D.F., Hassini, E. A Literature Review on Correlation Clustering: Cross-disciplinary Taxonomy with Bibliometric Analysis. Oper. Res. Forum 3, 47 (2022). https://doi.org/10.1007/s43069-022-00156-6

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