A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
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DOI：
10.1037/met0000095
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发表时间：
2017-09
影响因子：
7
通讯作者：
Steinley D
中科院分区：
文献类型：
--
作者：
Brusco MJ;Shireman E;Steinley D
在心理学研究中，基于一组二分变量上的测量来划分对象集合的问题是一个公认的问题，其应用包括临床诊断、教育测试、认知分类和选择分析。潜在类分析和K-均值聚类是心理学文献中流行的基于二分度量的对象划分方法。K-Medium聚类法最近被吹捧为一种潜在的心理数据有用工具，当变量衡量标准是二分法时，K-Means方法可能比它的近邻K-Means更可取。我们对潜在类方法、K-均值方法和K-中值方法进行了基于模拟的比较，以分割二分数据。虽然这三种方法都能够恢复聚类结构，但K-中值聚类的平均性能最好，其次是潜在类分析。我们还报告了这三种方法在应用于传递性推理数据的上下文中的结果，其中发现，当应用于真实数据时，这三种方法可以显示出深刻的差异。
The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all three methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the three methods within the context of an application to transitive reasoning data, where it was found that the three approaches can exhibit profound differences when applied to real data.
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影响因子：
6.4
作者：
Andrews, Rick L.;Brusco, Michael J.;Currim, Imran S.
通讯作者：
Currim, Imran S.
DOI：
10.1016/j.ijresmar.2003.04.001
发表时间：
2003-12-01
影响因子：
7
作者：
Andrews, RL;Currim, IS
通讯作者：
Currim, IS
影响因子：
3
作者：
ARABIE, P;CARROLL, JD
通讯作者：
CARROLL, JD
影响因子：
1.9
作者：
BEZDEK, JC;CORAY, C;WATSON, J
通讯作者：
WATSON, J
影响因子：
4.9
作者：
Bradshaw, Catherine P.;Waasdorp, Tracy E.;O'Brennan, Lindsey M.
通讯作者：
O'Brennan, Lindsey M.