IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2007i1p520-536.html
   My bibliography  Save this article

Model-based methods to identify multiple cluster structures in a data set

Author

Listed:
  • Galimberti, Giuliano
  • Soffritti, Gabriele

Abstract

No abstract is available for this item.

Suggested Citation

  • Galimberti, Giuliano & Soffritti, Gabriele, 2007. "Model-based methods to identify multiple cluster structures in a data set," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 520-536, September.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:520-536
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00075-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Belitskaya-Levy Ilana, 2006. "A Generalized Clustering Problem, with Application to DNA Microarrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-26, January.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    5. Kojadinovic, Ivan, 2004. "Agglomerative hierarchical clustering of continuous variables based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 269-294, June.
    6. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    7. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    8. Jerome H. Friedman & Jacqueline J. Meulman, 2004. "Clustering objects on subsets of attributes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 815-849, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Holland, E.P. & Burrow, J.F. & Dytham, C. & Aegerter, J.N., 2009. "Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics," Ecological Modelling, Elsevier, vol. 220(9), pages 1203-1217.
    2. Diani, Cecilia & Galimberti, Giuliano & Soffritti, Gabriele, 2022. "Multivariate cluster-weighted models based on seemingly unrelated linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    3. Vincent Vandewalle, 2020. "Multi-Partitions Subspace Clustering," Mathematics, MDPI, vol. 8(4), pages 1-18, April.
    4. Marbac, Matthieu & Vandewalle, Vincent, 2019. "A tractable multi-partitions clustering," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 167-179.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julian Rossbroich & Jeffrey Durieux & Tom F. Wilderjans, 2022. "Model Selection Strategies for Determining the Optimal Number of Overlapping Clusters in Additive Overlapping Partitional Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 264-301, July.
    2. 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".
    3. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    4. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," 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. 8(1), pages 45-61, March.
    5. Shaikh Mateen & McNicholas Paul D & Desmond Anthony F, 2010. "A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-17, March.
    6. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    7. Martínez-Ventura, Constanza & Mariño-Martínez, Ricardo & Miguélez-Márquez, Javier, 2023. "Redundancy of Centrality Measures in Financial Market Infrastructures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    8. Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
    9. 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.
    10. M. Velden & A. Iodice D’Enza & F. Palumbo, 2017. "Cluster Correspondence Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 158-185, March.
    11. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    12. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    13. Wang, Xiaogang & Qiu, Weiliang & Zamar, Ruben H., 2007. "CLUES: A non-parametric clustering method based on local shrinking," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 286-298, September.
    14. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    15. Bocci, Laura & Vicari, Donatella & Vichi, Maurizio, 2006. "A mixture model for the classification of three-way proximity data," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1625-1654, April.
    16. Lian, Heng, 2010. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1728-1737, August.
    17. Seoung Bum Kim & Jung Woo Lee & Sin Young Kim & Deok Won Lee, 2013. "Dental Informatics to Characterize Patients with Dentofacial Deformities," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    18. 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.
    19. Coffey, N. & Hinde, J. & Holian, E., 2014. "Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 14-29.
    20. Francisco de A. T. Carvalho & Antonio Irpino & Rosanna Verde & Antonio Balzanella, 2022. "Batch Self-Organizing Maps for Distributional Data with an Automatic Weighting of Variables and Components," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 343-375, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:520-536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.