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An algorithm for clusterwise linear regression based on smoothing techniques

journal contribution
posted on 2015-02-01, 00:00 authored by A M Bagirov, Julien UgonJulien Ugon, H G Mirzayeva
© 2014, Springer-Verlag Berlin Heidelberg. We propose an algorithm based on an incremental approach and smoothing techniques to solve clusterwise linear regression (CLR) problems. This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate an initial solution for solving global optimization problems at each iteration of the incremental algorithm. Such an approach allows one to find global or approximate global solutions to the CLR problems. The algorithm is tested using several data sets for regression analysis and compared with the multistart and incremental Späth algorithms.

History

Journal

Optimization letters

Volume

9

Issue

2

Pagination

375 - 390

Publisher

Springer

Location

New York, N.Y.

ISSN

1862-4472

eISSN

1862-4480

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, Springer-Verlag