IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v32y2015i2p176-211.html
   My bibliography  Save this article

Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries

Author

Listed:
  • Chiheb-Eddine N’Cir
  • Nadia Essoussi
  • Mohamed Limam

Abstract

Detecting overlapping structures and identifying non-linearly-separable clusters with complex shapes are two major issues in clustering. This paper presents two kernel based methods that produce overlapping clusters with both linear and nonlinear boundaries. To improve separability of input patterns, we used for both methods Mercer kernel technique. First, we propose Kernel Overlapping K-means I (KOKMI), a centroid based method, generalizing kernel K-means to produce nondisjoint clusters with nonlinear separations. Second, we propose Kernel Overlapping K-means II (KOKMII), a medoid based method improving the previous method in terms of efficiency and complexity. Experiments performed on non-linearly-separable and real multi-labeled data sets show that proposed learning methods outperform the existing ones. Copyright Classification Society of North America 2015

Suggested Citation

  • Chiheb-Eddine N’Cir & Nadia Essoussi & Mohamed Limam, 2015. "Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 176-211, July.
    • Handle: RePEc:spr:jclass:v:32:y:2015:i:2:p:176-211
    DOI: 10.1007/s00357-015-9181-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00357-015-9181-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00357-015-9181-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    2. Pascal Hattum & Herbert Hoijtink, 2009. "Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 297-328, December.
    3. Boris Mirkin, 1990. "A sequential fitting procedure for linear data analysis models," Journal of Classification, Springer;The Classification Society, vol. 7(2), pages 167-195, September.
    4. Depril, Dirk & Van Mechelen, Iven & Mirkin, Boris, 2008. "Algorithms for additive clustering of rectangular data tables," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4923-4938, July.
    5. B. Mirkin, 1987. "Additive clustering and qualitative factor analysis methods for similarity matrices," Journal of Classification, Springer;The Classification Society, vol. 4(1), pages 7-31, March.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    3. Tom Wilderjans & Dirk Depril & Iven Mechelen, 2012. "Block-Relaxation Approaches for Fitting the INDCLUS Model," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 277-296, October.
    4. Anil Chaturvedi & J. Carroll, 1994. "An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models," Journal of Classification, Springer;The Classification Society, vol. 11(2), pages 155-170, September.
    5. Gianfranco DI VAIO & Michele BATTISTI, 2010. "A Spatially-Filtered Mixture of Beta-Convergence Regression for EU Regions, 1980-2002," Regional and Urban Modeling 284100013, EcoMod.
    6. Frenkel Ter Hofstede & Michel Wedel & Jan-Benedict E.M. Steenkamp, 2002. "Identifying Spatial Segments in International Markets," Marketing Science, INFORMS, vol. 21(2), pages 160-177, July.
    7. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    8. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "How To Group Market Participants? Heterogeneity In Hedging Behavior," 2003 Annual meeting, July 27-30, Montreal, Canada 21963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Sphiwe B. Skhosana & Salomon M. Millard & Frans H. J. Kanfer, 2023. "A Novel EM-Type Algorithm to Estimate Semi-Parametric Mixtures of Partially Linear Models," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    10. 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.
    11. Heungsun Hwang & Marc Tomiuk, 2010. "Fuzzy clusterwise quasi-likelihood generalized linear 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. 4(4), pages 255-270, December.
    12. M. Battisti & F. Belloc & M. Del Gatto, 2017. "Technology-specific Production Functions," Working Paper CRENoS 201709, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Di Vaio, Gianfranco & Enflo, Kerstin, 2011. "Did globalization drive convergence? Identifying cross-country growth regimes in the long run," European Economic Review, Elsevier, vol. 55(6), pages 832-844, August.
    14. Wu, Qiang & Yao, Weixin, 2016. "Mixtures of quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 162-176.
    15. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H., 2011. "Accounting for Heterogeneity in Hedging Behavior: Comparing & Evaluating Grouping Methods," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114787, European Association of Agricultural Economists.
    16. Vincent Boucher & Carlo L. Del Bello & Fabrizio Panebianco & Thierry Verdier & Yves Zenou, 2023. "Education Transmission and Network Formation," Journal of Labor Economics, University of Chicago Press, vol. 41(1), pages 129-173.
    17. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    18. Atefeh Zarei & Zahra Khodadadi & Mohsen Maleki & Karim Zare, 2023. "Robust mixture regression modeling based on two-piece scale mixtures of normal 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. 17(1), pages 181-210, March.
    19. Philip Kostov & Sophia Davidova, 2023. "Smallholders Are Not the Same: Under the Hood of Kosovo Agriculture," Land, MDPI, vol. 12(1), pages 1-16, January.
    20. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.

    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:spr:jclass:v:32:y:2015:i:2:p:176-211. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.