A gradient descent based heuristic for solving regression clustering problems

Name: A gradient descent based heuristic for solving regression clustering problems
Author: Kayış, Enis

İsim	A gradient descent based heuristic for solving regression clustering problems
Yazar	Kayış, Enis
Basım Tarihi:	2020
Basım Yeri	- SciTePress
Konu	Regression clustering, Heuristics, Gradient descent
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989758440-4
Kayıt Numarası	19941e37-8f6a-42be-a7fb-e360c0f50abf
Lokasyon	Industrial Engineering
Tarih	2020
Örnek Metin	Regression analysis is the method of quantifying the effects of a set of independent variables on a dependent variable. In regression clustering problems, the data points with similar regression estimates are grouped into the same cluster either due to a business need or to increase the statistical significance of the resulting regression estimates. In this paper, we consider an extension of this problem where data points belonging to the same level of another partitioning categorical variable should belong to the same partition. Due to the combinatorial nature of this problem, an exact solution is computationally prohibitive. We provide an integer programming formulation and offer gradient descent based heuristic to solve this problem. Through simulated datasets, we analyze the performance of our heuristic across a variety of different settings. In our computational study, we find that our heuristic provides remarkably better solutions than the benchmark method within a reasonable time. Albeit the slight decrease in the performance as the number of levels increase, our heuristic provides good solutions when each of the true underlying partition has a similar number of levels.

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A gradient descent based heuristic for solving regression clustering problems

Yazar Kayış, Enis

Basım Tarihi 2020

Basım Yeri - SciTePress

Konu Regression clustering, Heuristics, Gradient descent

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989758440-4

Kayıt Numarası 19941e37-8f6a-42be-a7fb-e360c0f50abf

Lokasyon Industrial Engineering

Tarih 2020

Örnek Metin Regression analysis is the method of quantifying the effects of a set of independent variables on a dependent variable. In regression clustering problems, the data points with similar regression estimates are grouped into the same cluster either due to a business need or to increase the statistical significance of the resulting regression estimates. In this paper, we consider an extension of this problem where data points belonging to the same level of another partitioning categorical variable should belong to the same partition. Due to the combinatorial nature of this problem, an exact solution is computationally prohibitive. We provide an integer programming formulation and offer gradient descent based heuristic to solve this problem. Through simulated datasets, we analyze the performance of our heuristic across a variety of different settings. In our computational study, we find that our heuristic provides remarkably better solutions than the benchmark method within a reasonable time. Albeit the slight decrease in the performance as the number of levels increase, our heuristic provides good solutions when each of the true underlying partition has a similar number of levels.