Relevant bibliographies by topics / Clusterwise linear regression

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Academic literature on the topic 'Clusterwise linear regression'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Clusterwise linear regression"

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Li, Ting, Xinyuan Song, Yingying Zhang, Hongtu Zhu, and Zhongyi Zhu. "Clusterwise functional linear regression models." Computational Statistics & Data Analysis 158 (June 2021): 107192. http://dx.doi.org/10.1016/j.csda.2021.107192.

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Galimberti, Giuliano, and Gabriele Soffritti. "Seemingly unrelated clusterwise linear regression." Advances in Data Analysis and Classification 14, no. 2 (August 12, 2019): 235–60. http://dx.doi.org/10.1007/s11634-019-00369-4.

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Joki, Kaisa, Adil M. Bagirov, Napsu Karmitsa, Marko M. Mäkelä, and Sona Taheri. "Clusterwise support vector linear regression." European Journal of Operational Research 287, no. 1 (November 2020): 19–35. http://dx.doi.org/10.1016/j.ejor.2020.04.032.

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Park, Young Woong, Yan Jiang, Diego Klabjan, and Loren Williams. "Algorithms for Generalized Clusterwise Linear Regression." INFORMS Journal on Computing 29, no. 2 (May 2017): 301–17. http://dx.doi.org/10.1287/ijoc.2016.0729.

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Späth, H. "Clusterwise linear least absolute deviations regression." Computing 37, no. 4 (December 1986): 371–77. http://dx.doi.org/10.1007/bf02251095.

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García-Escudero, L. A., A. Gordaliza, A. Mayo-Iscar, and R. San Martín. "Robust clusterwise linear regression through trimming." Computational Statistics & Data Analysis 54, no. 12 (December 2010): 3057–69. http://dx.doi.org/10.1016/j.csda.2009.07.002.

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Wedel, Michel, and Cor Kistemaker. "Consumer benefit segmentation using clusterwise linear regression." International Journal of Research in Marketing 6, no. 1 (September 1989): 45–59. http://dx.doi.org/10.1016/0167-8116(89)90046-3.

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Hennig, C. "Identifiablity of Models for Clusterwise Linear Regression." Journal of Classification 17, no. 2 (July 1, 2000): 273–96. http://dx.doi.org/10.1007/s003570000022.

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Khadka, Mukesh, and Alexander Paz. "Comprehensive Clusterwise Linear Regression for Pavement Management Systems." Journal of Transportation Engineering, Part B: Pavements 143, no. 4 (December 2017): 04017014. http://dx.doi.org/10.1061/jpeodx.0000009.

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Di Mari, Roberto, Roberto Rocci, and Stefano Antonio Gattone. "Clusterwise linear regression modeling with soft scale constraints." International Journal of Approximate Reasoning 91 (December 2017): 160–78. http://dx.doi.org/10.1016/j.ijar.2017.09.006.

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Dissertations / Theses on the topic "Clusterwise linear regression"

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Mirzayeva, Hijran. "Nonsmooth optimization algorithms for clusterwise linear regression." Thesis, University of Ballarat, 2013. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41975.

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Data mining is about solving problems by analyzing data that present in databases. Supervised and unsupervised data classification (clustering) are among the most important techniques in data mining. Regression analysis is the process of fitting a function (often linear) to the data to discover how one or more variables vary as a function of another. The aim of clusterwise regression is to combine both of these techniques, to discover trends within data, when more than one trend is likely to exist. Clusterwise regression has applications for instance in market segmentation, where it allows one to gather information on customer behaviors for several unknown groups of customers. There exist different methods for solving clusterwise linear regression problems. In spite of that, the development of efficient algorithms for solving clusterwise linear regression problems is still an important research topic. In this thesis our aim is to develop new algorithms for solving clusterwise linear regression problems in large data sets based on incremental and nonsmooth optimization approaches. Three new methods for solving clusterwise linear regression problems are developed and numerically tested on publicly available data sets for regression analysis. The first method is a new algorithm for solving the clusterwise linear regression problems based on their nonsmooth nonconvex formulation. This is an incremental algorithm. The second method is a nonsmooth optimization algorithm for solving clusterwise linear regression problems. Nonsmooth optimization techniques are proposed to use instead of the Sp¨ath algorithm to solve optimization problems at each iteration of the incremental algorithm. The discrete gradient method is used to solve nonsmooth optimization problems at each iteration of the incremental algorithm. This approach allows one to reduce the CPU time and the number of regression problems solved in comparison with the first incremental algorithm. The third algorithm is an algorithm based on an incremental approach and on the smoothing techniques for solving clusterwise linear regression problems. The use of smoothing techniques allows one to apply powerful methods of smooth nonlinear programming to solve clusterwise linear regression problems. Numerical results are presented for all three algorithms using small to large data sets. The new algorithms are also compared with multi-start Sp¨ath algorithm for clusterwise linear regression.
Doctor of Philosophy
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Mahmood, Arshad. "Rainfall prediction in Australia : Clusterwise linear regression approach." Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251.

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Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
Doctor of Philosophy
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SILVA, Ricardo Azevedo Moreira da. "Combinando regressão linear clusterwise e k-means com ponderação automática das variáveis explicativas." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/26011.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-08-22T19:08:28Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Ricardo Azevedo Moreira da Silva.pdf: 3866848 bytes, checksum: 4fb1080cdd975057fc1dff03a0338e22 (MD5)
Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-08-29T19:40:07Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Ricardo Azevedo Moreira da Silva.pdf: 3866848 bytes, checksum: 4fb1080cdd975057fc1dff03a0338e22 (MD5)
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Este trabalho propõe um método de regressão linear do tipo clusterwise cujo objetivo é fornecer modelos de regressão linear baseados em grupos homogêneos de observações em relação às variáveis explicativas e que são bem ajustados em relação à variável de resposta. Para atingir esse objetivo, este método combina o método regressão linear do tipo clusterwise padrão e o método de agrupamento K-means com a ponderação automática das variáveis explicativas. Os pesos das variáveis explicativas mudam em cada iteração do algoritmo e são diferentes de uma variável para outra. Assim, este método é capaz de selecionar as variáveis relevantes na busca por clusters homogêneos em relação às variáveis explicativas. Por fim, uma vez que ele aprende simultaneamente um protótipo de grupo e um modelo de regressão linear para cada cluster, ele é capaz de atribuir um modelo de regressão apropriado para uma observação desconhecida com base na sua descrição através de suas variáveis explicativas. Experimentos com conjuntos de dados sintéticos e reais corroboram a utilidade do método proposto.
This work gives a linear regression method of the clusterwise type aiming to provide linear regression models that are based on homogeneous clusters of observations w.r.t. the explanatory variables and that are well fitted w.r.t. the response variable. To achieve this goal, this method combines the standard clusterwise linear regression method and the K-means clustering method with the automatic weighting of the explanatory variables. The relevance weights of the explanatory variables change in each iteration of the algorithm and are different from one variable to another. Thus, this method is able to select the relevant variables in the search for homogeneous clusters w.r.t. the explanatory variables. Finally, since it simultaneously learns a prototype and a linear regression model for each cluster, this method is able to assign an appropriate regression model to an unknown observation based on its description through its explanatory variables. Experiments with synthetic and real datasets corroborate the utility of the proposed method.
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4

"Least median squares algorithm for clusterwise linear regression." 2009. http://library.cuhk.edu.hk/record=b5894193.

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Fung, Chun Yip.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 53-54).
Abstract also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- The Exchange Algorithm Framework --- p.4
Chapter 2.1 --- Ordinary Least Squares Linear Regression --- p.5
Chapter 2.2 --- The Exchange Algorithm --- p.6
Chapter 3 --- Methodology --- p.12
Chapter 3.1 --- Least Median Squares Linear Regression --- p.12
Chapter 3.2 --- Least Median Squares Algorithm for Clusterwise Linear Re- gression --- p.16
Chapter 3.3 --- Measures of Performance --- p.20
Chapter 3.4 --- An Illustrative Example --- p.24
Chapter 4 --- Monte Carlo Simulation Study --- p.34
Chapter 4.1 --- Simulation Plan --- p.34
Chapter 4.2 --- Simulation Results --- p.41
Chapter 4.2.1 --- Effects of the Six factors --- p.41
Chapter 4.2.2 --- Comparisons between LMSA and the Exchange Algorithm --- p.47
Chapter 4.2.3 --- Evaluation of the Improvement of Regression Parame- ters by Performing Stage 3 in LMSA --- p.50
Chapter 5 --- Concluding Remarks --- p.51
Bibliography --- p.52
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Book chapters on the topic "Clusterwise linear regression"

1

Hennig, C. "Models and Methods for Clusterwise Linear Regression." In Studies in Classification, Data Analysis, and Knowledge Organization, 179–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60187-3_17.

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Karmitsa, Napsu, Adil M. Bagirov, Sona Taheri, and Kaisa Joki. "Limited Memory Bundle Method for Clusterwise Linear Regression." In Intelligent Systems, Control and Automation: Science and Engineering, 109–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70787-3_8.

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Preda, Cristian, and Gilbert Saporta. "PLS Approach for Clusterwise Linear Regression on Functional Data." In Classification, Clustering, and Data Mining Applications, 167–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_17.

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Di Mari, Roberto, Stefano Antonio Gattone, and Roberto Rocci. "Penalized Versus Constrained Approaches for Clusterwise Linear Regression Modeling." In Statistical Learning and Modeling in Data Analysis, 89–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69944-4_10.

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Kayış, Enis. "Designing an Efficient Gradient Descent Based Heuristic for Clusterwise Linear Regression for Large Datasets." In Communications in Computer and Information Science, 154–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83014-4_8.

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da Silva, Ricardo A. M., and Francisco de A. T. de Carvalho. "On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables." In Artificial Neural Networks and Machine Learning – ICANN 2017, 402–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_46.

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