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Comparing partitions

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Cited by:

  1. Luca Marotta & Salvatore Miccichè & Yoshi Fujiwara & Hiroshi Iyetomi & Hideaki Aoyama & Mauro Gallegati & Rosario N Mantegna, 2015. "Bank-Firm Credit Network in Japan: An Analysis of a Bipartite Network," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
  2. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
  3. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
  4. Michael P. B. Gallaugher & Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2022. "Multivariate cluster weighted models using skewed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 93-124, March.
  5. Michael C. Thrun & Alfred Ultsch, 2021. "Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 280-312, July.
  6. Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 485-512, June.
  7. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
  8. Laura Bocci & Donatella Vicari, 2019. "ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 941-985, December.
  9. Hui Li & Cory R. Brouwer & Weijun Luo, 2022. "A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  10. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
  11. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
  12. Zhongnan Jin & Jie Min & Yili Hong & Pang Du & Qingyu Yang, 2024. "Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems," INFORMS Joural on Data Science, INFORMS, vol. 3(2), pages 203-218, October.
  13. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
  14. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
  15. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
  16. Browne, Ryan P., 2022. "Revitalizing the multivariate elliptical leptokurtic-normal distribution and its application in model-based clustering," Statistics & Probability Letters, Elsevier, vol. 190(C).
  17. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 295-304, June.
  18. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
  19. Irene Vrbik & Paul McNicholas, 2015. "Fractionally-Supervised Classification," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 359-381, October.
  20. Kyusoon Kim & Hee‐Seok Oh & Minsu Park, 2023. "Principal component analysis for river network data: Use of spatiotemporal correlation and heterogeneous covariance structure," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.
  21. Maurizio Vichi & Carlo Cavicchia & Patrick J. F. Groenen, 2022. "Hierarchical Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 553-577, November.
  22. Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
  23. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  24. Binhuan Wang & Lanqiu Yao & Jiyuan Hu & Huilin Li, 2023. "A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 193-216, April.
  25. Christian Hennig, 2022. "An empirical comparison and characterisation of nine popular clustering methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 201-229, March.
  26. Chen, Yuanxing & Fang, Kuangnan & Lan, Wei & Tsai, Chih-Ling & Zhang, Qingzhao, 2025. "Community influence analysis in social networks," Computational Statistics & Data Analysis, Elsevier, vol. 202(C).
  27. Jajcay, Nikola, 2018. "Spatial and temporal scales of atmospheric dynamics," Thesis Commons ar8ks, Center for Open Science.
  28. Patrick D. Shay & Stephen S. Farnsworth Mick, 2017. "Clustered and distinct: a taxonomy of local multihospital systems," Health Care Management Science, Springer, vol. 20(3), pages 303-315, September.
  29. Eva Vande Gaer & Eva Ceulemans & Iven Mechelen & Peter Kuppens, 2012. "The CLASSI-N Method for the Study of Sequential Processes," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 85-105, January.
  30. Haohao Su & Yuesong Wu & Bin Chen & Yuehua Cui, 2025. "STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
  31. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
  32. Yue Wan & Jialu Wu & Tingjun Hou & Chang-Yu Hsieh & Xiaowei Jia, 2025. "Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  33. M. Velden & A. Iodice D’Enza & F. Palumbo, 2017. "Cluster Correspondence Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 158-185, March.
  34. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
  35. Matthijs Warrens, 2010. "Inequalities Between Kappa and Kappa-Like Statistics for k×k Tables," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 176-185, March.
  36. Renato Cordeiro Amorim & Vladimir Makarenkov & Boris Mirkin, 2020. "Core Clustering as a Tool for Tackling Noise in Cluster Labels," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 143-157, April.
  37. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
  38. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
  39. Bel, François & Lacroix, Anne & Lyser, Sandrine & Rambonilaza, Tina & Turpin, Nadine, 2015. "Domestic demand for tourism in rural areas: Insights from summer stays in three French regions," Tourism Management, Elsevier, vol. 46(C), pages 562-570.
  40. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
  41. Deborah Miori & Mihai Cucuringu, 2022. "DeFi: data-driven characterisation of Uniswap v3 ecosystem & an ideal crypto law for liquidity pools," Papers 2301.13009, arXiv.org, revised Jan 2023.
  42. M. Mucciardi & E. Otranto, 2016. "A Flexible Specification of Space–Time AutoRegressive Models," Working Paper CRENoS 201608, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  43. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
  44. Xuehai Wang & Michael Nissen & Deanne Gracias & Manabu Kusakabe & Guillermo Simkin & Aixiang Jiang & Gerben Duns & Clementine Sarkozy & Laura Hilton & Elizabeth A. Chavez & Gabriela C. Segat & Rachel , 2022. "Single-cell profiling reveals a memory B cell-like subtype of follicular lymphoma with increased transformation risk," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  45. Ken Deal, 2014. "Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 7(1), pages 5-21, March.
  46. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
  47. Michael Fop & Pierre-Alexandre Mattei & Charles Bouveyron & Thomas Brendan Murphy, 2022. "Unobserved classes and extra variables in high-dimensional discriminant analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 55-92, March.
  48. Minseog Oh & Donggyu Kim, 2024. "Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups," Papers 2412.05664, arXiv.org.
  49. Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2021. "Matrix Normal Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 556-575, October.
  50. Carleo, Alessandra & Rocci, Roberto, 2024. "Functional clustering of NPLs recovery curves," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  51. Evelina Gabasova & John Reid & Lorenz Wernisch, 2017. "Clusternomics: Integrative context-dependent clustering for heterogeneous datasets," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-29, October.
  52. Genane Youness & Gilbert Saporta, 2010. "Comparing partitions of two sets of units based on the same variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(1), pages 53-64, April.
  53. Tsai, Chieh-Yuan & Chiu, Chuang-Cheng, 2008. "Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4658-4672, June.
  54. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.
  55. Sun, Heli & Liu, Jiao & Huang, Jianbin & Wang, Guangtao & Yang, Zhou & Song, Qinbao & Jia, Xiaolin, 2015. "CenLP: A centrality-based label propagation algorithm for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 767-780.
  56. Wan-Lun Wang & Yu-Chen Yang & Tsung-I Lin, 2024. "Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 271-307, June.
  57. Volodymyr Melnykov & Xuwen Zhu, 2019. "An extension of the K-means algorithm to clustering skewed data," Computational Statistics, Springer, vol. 34(1), pages 373-394, March.
  58. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
  59. Hai-Yun Xu & Zeng-Hui Yue & Chao Wang & Kun Dong & Hong-Shen Pang & Zhengbiao Han, 2017. "Multi-source data fusion study in scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 773-792, May.
  60. Matthijs Warrens, 2008. "On Similarity Coefficients for 2×2 Tables and Correction for Chance," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 487-502, September.
  61. A van Giessen & K G M Moons & G A de Wit & W M M Verschuren & J M A Boer & H Koffijberg, 2015. "Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-14, January.
  62. Guang Ouyang & Dipak K. Dey & Panpan Zhang, 2020. "Clique-Based Method for Social Network Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 254-274, April.
  63. Ramirez-Marquez, J.E. & Rocco, C.M. & Moronta, J. & Gama Dessavre, D., 2016. "Robustness in network community detection under links weights uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 88-95.
  64. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
  65. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2014. "Mixtures of skew-t factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 326-335.
  66. Preeti & Kusum Deep, 2024. "Density Peak Clustering Using Grey Wolf Optimization Approach," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 338-370, July.
  67. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.
  68. Siow Hoo Leong & Seng Huat Ong, 2017. "Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-30, July.
  69. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
  70. Md Tauhidul Islam & Lei Xing, 2023. "Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  71. Shao, Lihui & Wu, Jiaqi & Zhang, Weiping & Chen, Yu, 2024. "Integrated subgroup identification from multi-source data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
  72. Grn, Bettina & Leisch, Friedrich, 2009. "Dealing with label switching in mixture models under genuine multimodality," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 851-861, May.
  73. Heath, Jeffrey W. & Fu, Michael C. & Jank, Wolfgang, 2009. "New global optimization algorithms for model-based clustering," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3999-4017, October.
  74. Jerzy Korzeniewski, 2013. "Empirical Evaluation of OCLUS and GenRandomClust Algorithms of Generating Cluster Structures," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 487-494, September.
  75. Carter Allen & Yuzhou Chang & Brian Neelon & Won Chang & Hang J. Kim & Zihai Li & Qin Ma & Dongjun Chung, 2023. "A Bayesian multivariate mixture model for high throughput spatial transcriptomics," Biometrics, The International Biometric Society, vol. 79(3), pages 1775-1787, September.
  76. Vincent Labatut & Jean-Michel Balasque, 2010. "Business-oriented Analysis of a Social Network of University Students," Post-Print hal-00633643, HAL.
  77. Viviana Amati & Silvia Meggiolaro & Giulia Rivellini & Susanna Zaccarin, 2017. "Relational Resources of Individuals Living in Couple: Evidence from an Italian Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(2), pages 547-590, November.
  78. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
  79. Stefano Tonellato & Andrea Pastore, 2013. "On the comparison of model-based clustering solutions," Working Papers 2013:05, Department of Economics, University of Venice "Ca' Foscari".
  80. Nicola G Criscuolo & Claudia Angelini, 2020. "StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
  81. Roberto Mari & Salvatore Ingrassia & Antonio Punzo, 2023. "Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 233-266, July.
  82. Andrzej Młodak, 2021. "k-Means, Ward and Probabilistic Distance-Based Clustering Methods with Contiguity Constraint," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 313-352, July.
  83. Ana Helena Tavares & Jakob Raymaekers & Peter J. Rousseeuw & Paula Brito & Vera Afreixo, 2020. "Clustering genomic words in human DNA using peaks and trends of distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 57-76, March.
  84. Katharine M. Clark & Paul D. McNicholas, 2024. "Finding Outliers in Gaussian Model-based Clustering," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 313-337, July.
  85. Wenhua Li & Junpeng Guo & Ying Chen & Minglu Wang, 2016. "A New Representation of Interval Symbolic Data and Its Application in Dynamic Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 149-165, April.
  86. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
  87. Florian Schreiber, 2017. "Identification of customer groups in the German term life market: a benefit segmentation," Annals of Operations Research, Springer, vol. 254(1), pages 365-399, July.
  88. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  89. Musciotto, Federico & Marotta, Luca & Miccichè, Salvatore & Piilo, Jyrki & Mantegna, Rosario N., 2016. "Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 267-278.
  90. Renato Cordeiro Amorim, 2016. "A Survey on Feature Weighting Based K-Means Algorithms," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 210-242, July.
  91. Oleg Sobchuk & Artjoms Šeļa, 2024. "Computational thematics: comparing algorithms for clustering the genres of literary fiction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
  92. Christina Yassouridis & Friedrich Leisch, 2017. "Benchmarking different clustering algorithms on functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 467-492, September.
  93. Marino, Maria Francesca & Pandolfi, Silvia, 2022. "Hybrid maximum likelihood inference for stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
  94. Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.
  95. Melnykov, Volodymyr & Melnykov, Igor, 2012. "Initializing the EM algorithm in Gaussian mixture models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1381-1395.
  96. Aghiles Salah & Mohamed Nadif, 2019. "Directional co-clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 591-620, September.
  97. Catania, Leopoldo & Di Mari, Roberto, 2021. "Hierarchical Markov-switching models for multivariate integer-valued time-series," Journal of Econometrics, Elsevier, vol. 221(1), pages 118-137.
  98. Paula M. Murray & Ryan P. Browne & Paul D. McNicholas, 2020. "Mixtures of Hidden Truncation Hyperbolic Factor Analyzers," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 366-379, July.
  99. Ruijin Lu & Boya Zhang & Anna Birukov & Cuilin Zhang & Zhen Chen, 2024. "A Variance-Based Sensitivity Analysis Approach for Identifying Interactive Exposures," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 520-541, July.
  100. Xinhai Liu & Wolfgang Glänzel & Bart Moor, 2012. "Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 473-493, May.
  101. Santi, Éverton & Aloise, Daniel & Blanchard, Simon J., 2016. "A model for clustering data from heterogeneous dissimilarities," European Journal of Operational Research, Elsevier, vol. 253(3), pages 659-672.
  102. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
  103. Melnykov, Volodymyr, 2016. "ClickClust: An R Package for Model-Based Clustering of Categorical Sequences," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i09).
  104. Juhyun Park & Jeongyoun Ahn, 2017. "Clustering multivariate functional data with phase variation," Biometrics, The International Biometric Society, vol. 73(1), pages 324-333, March.
  105. Tom Wilderjans & Eva Ceulemans & Iven Mechelen, 2008. "The CHIC Model: A Global Model for Coupled Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 729-751, December.
  106. İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.
  107. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
  108. Elizabeth Ayers & Sophia Rabe-Hesketh & Rebecca Nugent, 2013. "Incorporating Student Covariates in Cognitive Diagnosis Models," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 195-224, July.
  109. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
  110. Yuchen Liang & Guowei Shi & Runlin Cai & Yuchen Yuan & Ziying Xie & Long Yu & Yingjian Huang & Qian Shi & Lizhe Wang & Jun Li & Zhonghui Tang, 2024. "PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  111. Vega, I. & Schütte, Ch. & Conrad, T.O.F., 2016. "Finding metastable states in real-world time series with recurrence networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 1-17.
  112. Jeffrey Andrews & Paul McNicholas, 2014. "Variable Selection for Clustering and Classification," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 136-153, July.
  113. Kensuke Tanioka & Hiroshi Yadohisa, 2019. "Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 73-93, April.
  114. Remizov, Alexey & Memon, Shazim Ali & Kim, Jong R., 2024. "Novel building energy performance-based climate zoning enhanced with spatial constraint," Applied Energy, Elsevier, vol. 355(C).
  115. Francesco Dotto & Alessio Farcomeni & Luis Angel García-Escudero & Agustín Mayo-Iscar, 2017. "A fuzzy approach to robust regression clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 691-710, December.
  116. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
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