A cluster differences unfolding method for large datasets of preference ratings on an interval scale: Minimizing the mean squared centred residuals

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Rodrigo Macías, J. Fernando Vera, Willem J. Heiser
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引用次数: 0

Abstract

Clustering and spatial representation methods are often used in combination, to analyse preference ratings when a large number of individuals and/or object is involved. When analysed under an unfolding model, row-conditional linear transformations are usually most appropriate when the goal is to determine clusters of individuals with similar preferences. However, a significant problem with transformations that include both slope and intercept is the occurrence of degenerate solutions. In this paper, we propose a least squares unfolding method that performs clustering of individuals while simultaneously estimating the location of cluster centres and object locations in low-dimensional space. The method is based on minimising the mean squared centred residuals of the preference ratings with respect to the distances between cluster centres and object locations. At the same time, the distances are row-conditionally transformed with optimally estimated slope parameters. It is computationally efficient for large datasets, and does not suffer from the appearance of degenerate solutions. The performance of the method is analysed in an extensive Monte Carlo experiment. It is illustrated for a real data set and the results are compared with those obtained using a two-step clustering and unfolding procedure.

用于区间尺度偏好评分大型数据集的聚类差异展开法:最小化居中残差均方。
当涉及大量个体和/或对象时,聚类和空间表示方法通常会结合使用,以分析偏好评级。在展开模型下进行分析时,当目标是确定具有相似偏好的个体聚类时,行条件线性变换通常是最合适的。然而,同时包含斜率和截距的变换的一个重要问题是会出现退化解。在本文中,我们提出了一种最小二乘展开法,在对个体进行聚类的同时,还能估计聚类中心的位置和低维空间中的对象位置。该方法基于最小化偏好评级与聚类中心和对象位置之间距离的均方中心残差。同时,利用最优估计的斜率参数对距离进行行条件变换。该方法对大型数据集的计算效率很高,而且不会出现退化解。通过大量的蒙特卡罗实验分析了该方法的性能。对一个真实数据集进行了说明,并将结果与使用两步聚类和展开程序获得的结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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