IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v50y2006i3p585-603.html
   My bibliography  Save this article

Separation index and partial membership for clustering

Author

Listed:
  • Qiu, Weiliang
  • Joe, Harry

Abstract

No abstract is available for this item.

Suggested Citation

  • Qiu, Weiliang & Joe, Harry, 2006. "Separation index and partial membership for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 585-603, February.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:3:p:585-603
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00274-9
    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. Dhillon, Inderjit S. & Modha, Dharmendra S. & Spangler, W. Scott, 2002. "Class visualization of high-dimensional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 59-90, November.
    2. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
    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. Dario Bruzzese & Domenico Vistocco, 2015. "DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 285-304, July.
    2. Abby Flynt & Nema Dean & Rebecca Nugent, 2019. "sARI: a soft agreement measure for class partitions incorporating assignment probabilities," 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(1), pages 303-323, March.
    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. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
    5. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    6. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.

    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. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    2. Laurent Lesnard, 2010. "Setting Cost in Optimal Matching to Uncover Contemporaneous Socio-Temporal Patterns," Sociological Methods & Research, , vol. 38(3), pages 389-419, February.
    3. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    4. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.
    5. Denisa Kocanova & Viliam Kovac & Vitaliy Serzhanov & Jan Buleca, 2023. "Differences and Similarities in Patterns of Ageing Society in the European Union," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 26(1), pages 45-64, March.
    6. Antony Paulraj, 2009. "Environmental motivations: a classification scheme and its impact on environmental strategies and practices," Business Strategy and the Environment, Wiley Blackwell, vol. 18(7), pages 453-468, November.
    7. Massimiliano Agovino & Maria Ferrara & Antonio Garofalo, 2017. "The driving factors of separate waste collection in Italy: a multidimensional analysis at provincial level," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(6), pages 2297-2316, December.
    8. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    9. Guidi, Lionel & Ibanez, Frédéric & Calcagno, Vincent & Beaugrand, Grégory, 2009. "A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data," Ecological Modelling, Elsevier, vol. 220(4), pages 451-461.
    10. Nickolay T. Trendafilov & Tsegay Gebrehiwot Gebru, 2016. "Recipes for sparse LDA of horizontal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 207-221, August.
    11. Glenn Milligan, 1985. "An algorithm for generating artificial test clusters," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 123-127, March.
    12. Laurent Lesnard, 2006. "Optimal Matching and Social Sciences," Working Papers 2006-01, Center for Research in Economics and Statistics.
    13. du Jardin, Philippe, 2021. "Forecasting corporate failure using ensemble of self-organizing neural networks," European Journal of Operational Research, Elsevier, vol. 288(3), pages 869-885.
    14. Luigi Ippoliti & Simone Di Zio & Arcangelo Merla, 2014. "Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1830-1847, August.
    15. Trendafilov, Nickolay T. & Vines, Karen, 2009. "Simple and interpretable discrimination," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 979-989, February.
    16. Weinand, J.M. & McKenna, R. & Fichtner, W., 2019. "Developing a municipality typology for modelling decentralised energy systems," Utilities Policy, Elsevier, vol. 57(C), pages 75-96.
    17. Ernst, Holger & Wickede, Anje, 1999. "Einflußfaktoren auf die Glaubwürdigkeit kundenorientierter Produkt-Vorankündigungen: Ein signaltheoretischer Ansatz," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 515, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    18. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
    19. Dario Bruzzese & Domenico Vistocco, 2015. "DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 285-304, July.
    20. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.

    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:50:y:2006:i:3:p:585-603. 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.