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.

References in zbMATH (referenced in 44 articles )

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  1. Liu, Fei; Billard, L.: Partition of interval-valued observations using regression (2022)
  2. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  3. Blanco, V.; Japón, A.; Ponce, D.; Puerto, J.: On the multisource hyperplanes location problem to fitting set of points (2021)
  4. de A. T. de Carvalho, Francisco; de A. Lima Neto, Eufrásio; da Silva, Kassio C. F.: A clusterwise nonlinear regression algorithm for interval-valued data (2021)
  5. Li, Ting; Song, Xinyuan; Zhang, Yingying; Zhu, Hongtu; Zhu, Zhongyi: Clusterwise functional linear regression models (2021)
  6. Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona: Clusterwise support vector linear regression (2020)
  7. Bagirov, A. M.; Ugon, J.: Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms (2018)
  8. Demidenko, Eugene: The next-generation (K)-means algorithm (2018)
  9. Bagirov, Adil M.; Taheri, Sona: DC programming algorithm for clusterwise linear (L_1) regression (2017)
  10. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
  11. Lim, Hwa Kyung; Narisetty, Naveen N.; Cheon, Sooyoung: Robust multivariate mixture regression models with incomplete data (2017)
  12. 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)
  13. Reis dos Santos, M. Isabel; Reis dos Santos, Pedro M.: Switching regression metamodels in stochastic simulation (2016)
  14. Zeller, Camila B.; Cabral, Celso R. B.; Lachos, Víctor H.: Robust mixture regression modeling based on scale mixtures of skew-normal distributions (2016)
  15. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran G.: Nonsmooth optimization algorithm for solving clusterwise linear regression problems (2015)
  16. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran G.: An algorithm for clusterwise linear regression based on smoothing techniques (2015)
  17. Manwani, Naresh; Sastry, P. S.: (K)-plane regression (2015)
  18. Yamashita, Naoto; Mayekawa, Shin-ichi: A new biplot procedure with joint classification of objects and variables by fuzzy (c)-means clustering (2015)
  19. Carbonneau, Réal A.; Caporossi, Gilles; Hansen, Pierre: Globally optimal clusterwise regression by column generation enhanced with heuristics, sequencing and ending subset optimization (2014)
  20. Bagirov, Adil M.; Ugon, Julien; Mirzayeva, Hijran: Nonsmooth nonconvex optimization approach to clusterwise linear regression problems (2013)

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