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Probabilistic models in cluster analysis

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  • Bock, Hans H.

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  • Bock, Hans H., 1996. "Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 5-28, November.
    • Handle: RePEc:eee:csdana:v:23:y:1996:i:1:p:5-28
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    16. H. Bock, 1985. "On some significance tests in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 77-108, December.
    17. Furman, W. David & Lindsay, Bruce G., 1994. "Testing for the number of components in a mixture of normal distributions using moment estimators," Computational Statistics & Data Analysis, Elsevier, vol. 17(5), pages 473-492, June.
    18. N. Sriram & Scott Lewis, 1993. "Constructing optimal ultrametrics," Journal of Classification, Springer;The Classification Society, vol. 10(2), pages 241-268, December.
    19. M. Dale & J. Moon, 1988. "Statistical tests on two characteristics of the shapes of cluster diagrams," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 21-38, March.
    20. Gilles Celeux & Gérard Govaert, 1991. "Clustering criteria for discrete data and latent class models," Journal of Classification, Springer;The Classification Society, vol. 8(2), pages 157-176, December.
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    1. Jan Schepers & Hans-Hermann Bock & Iven Mechelen, 2017. "Maximal Interaction Two-Mode Clustering," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 49-75, April.
    2. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," 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. 10(2), pages 209-224, June.
    3. Zaheer Ahmed & Alberto Cassese & Gerard Breukelen & Jan Schepers, 2021. "REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction," 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. 15(4), pages 987-1013, December.
    4. Tsai, Cary Chi-Liang & Cheng, Echo Sihan, 2021. "Incorporating statistical clustering methods into mortality models to improve forecasting performances," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 42-62.
    5. Zaheer Ahmed & Alberto Cassese & Gerard Breukelen & Jan Schepers, 2023. "E-ReMI: Extended Maximal Interaction Two-mode Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 298-331, July.
    6. Bouveyron, C. & Girard, S. & Schmid, C., 2007. "High-dimensional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 502-519, September.
    7. Sanjeena Subedi & Paul D. McNicholas, 2021. "A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 89-108, April.
    8. Alessio Farcomeni, 2009. "Robust Double Clustering: A Method Based on Alternating Concentration Steps," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 77-101, April.
    9. repec:jss:jstsof:46:i06 is not listed on IDEAS
    10. Pronello, Cristina & Camusso, Cristian, 2011. "Travellers’ profiles definition using statistical multivariate analysis of attitudinal variables," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1294-1308.
    11. Diana, Marco & Pronello, Cristina, 2010. "Traveler segmentation strategy with nominal variables through correspondence analysis," Transport Policy, Elsevier, vol. 17(3), pages 183-190, May.
    12. Komárek, Arnošt & Komárková, Lenka, 2014. "Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i12).
    13. Schepers, Jan & van Mechelen, Iven & Ceulemans, Eva, 2006. "Three-mode partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1623-1642, December.
    14. Jukka Corander & Mats Gyllenberg & Timo Koski, 2009. "Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy," 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. 3(1), pages 3-24, June.
    15. Naoto Yamashita & Kohei Adachi, 2020. "A Modified k-Means Clustering Procedure for Obtaining a Cardinality-Constrained Centroid Matrix," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 509-525, July.
    16. Bachmann, Sascha, 2016. "Concentration for Poisson functionals: Component counts in random geometric graphs," Stochastic Processes and their Applications, Elsevier, vol. 126(5), pages 1306-1330.
    17. Luis García-Escudero & Alfonso Gordaliza & Carlos Matrán & Agustín Mayo-Iscar, 2010. "A review of robust 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. 4(2), pages 89-109, September.
    18. Weiß, Christian H. & Göb, Rainer, 2008. "Discovering patterns in categorical time series using IFS," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4369-4379, May.

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