Algorithm 39

Algorithm 39: Clusterwise linear regression. The combinatorial problem of clusterwise discrete linear approximation is defined as finding a given number of clusters of observations such that the overall sum of error sum of squares within those clusters becomes a minimum. The FORTRAN implementation of a heuristic solution method and a numerical example are given. Algorithm 48: a fast algorithm for clusterwise linear regression.

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References in zbMATH (referenced in 39 articles )

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  1. Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona: Clusterwise support vector linear regression (2020)
  2. Bagirov, A. M.; Ugon, J.: Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms (2018)
  3. Demidenko, Eugene: The next-generation (K)-means algorithm (2018)
  4. Bagirov, Adil M.; Taheri, Sona: DC programming algorithm for clusterwise linear (L_1) regression (2017)
  5. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
  6. Lim, Hwa Kyung; Narisetty, Naveen N.; Cheon, Sooyoung: Robust multivariate mixture regression models with incomplete data (2017)
  7. Wilderjans, Tom Frans; Vande Gaer, Eva; Kiers, Henk A. L.; Van Mechelen, Iven; Ceulemans, Eva: Principal covariates clusterwise regression (PCCR): accounting for multicollinearity and population heterogeneity in hierarchically organized data (2017)
  8. Reis dos Santos, M. Isabel; Reis dos Santos, Pedro M.: Switching regression metamodels in stochastic simulation (2016)
  9. Zeller, Camila B.; Cabral, Celso R. B.; Lachos, Víctor H.: Robust mixture regression modeling based on scale mixtures of skew-normal distributions (2016)
  10. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran G.: An algorithm for clusterwise linear regression based on smoothing techniques (2015)
  11. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran G.: Nonsmooth optimization algorithm for solving clusterwise linear regression problems (2015)
  12. Manwani, Naresh; Sastry, P. S.: (K)-plane regression (2015)
  13. Yamashita, Naoto; Mayekawa, Shin-ichi: A new biplot procedure with joint classification of objects and variables by fuzzy (c)-means clustering (2015)
  14. Carbonneau, Réal A.; Caporossi, Gilles; Hansen, Pierre: Globally optimal clusterwise regression by column generation enhanced with heuristics, sequencing and ending subset optimization (2014)
  15. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran: Nonsmooth nonconvex optimization approach to clusterwise linear regression problems (2013)
  16. Tan, Tianyu; Suk, Hye Won; Hwang, Heungsun; Lim, Jooseop: Functional fuzzy clusterwise regression analysis (2013)
  17. Vicari, Donatella; Vichi, Maurizio: Multivariate linear regression for heterogeneous data (2013)
  18. Carbonneau, Réal A.; Caporossi, Gilles; Hansen, Pierre: Extensions to the repetitive branch and bound algorithm for globally optimal clusterwise regression (2012)
  19. Köhn, Hans-Friedrich: A review of multiobjective programming and its application in quantitative psychology (2011)
  20. Qian, Guoqi; Wu, Yuehua: Estimation and selection in regression clustering (2011)

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