KSC-N: Clustering of Hierarchical Time Profile Data

被引：8

作者：

Heylen, Joke ^{[1
]}

Van Mechelen, Iven ^{[1
]}

Verduyn, Philippe ^{[1
]}

Ceulemans, Eva ^{[1
]}

机构：

[1] Univ Leuven, B-3000 Louvain, Belgium

来源：

PSYCHOMETRIKA | 2016年 / 81卷 / 02期

关键词：

hierarchical data; time profiles; shape and amplitude variability; individual differences; clustering; KSC; EMOTIONAL EXPERIENCE; LANGUAGE IMPAIRMENT; INTENSITY PROFILES; COMPONENT ANALYSIS; DISORDER; INFORMATION; DEPRESSION; INERTIA; MODELS;

D O I：

10.1007/s11336-014-9433-x

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Quite a few studies in the behavioral sciences result in hierarchical time profile data, with a number of time profiles being measured for each person under study. Associated research questions often focus on individual differences in profile repertoire, that is, differences between persons in the number and the nature of profile shapes that show up for each person. In this paper, we introduce a new method, called KSC-N, that parsimoniously captures such differences while neatly disentangling variability in shape and amplitude. KSC-N induces a few person clusters from the data and derives for each person cluster the types of profile shape that occur most for the persons in that cluster. An algorithm for fitting KSC-N is proposed and evaluated in a simulation study. Finally, the new method is applied to emotional intensity profile data.

引用

页码：411 / 433

页数：23

共 36 条

[1]

[Anonymous], 2011, P ACM INT C WEB SEAR, DOI DOI 10.1145/1935826.1935863

[2] ConPar: a method for identifying groups of concordant subject proximity matrices for subsequent multidimensional scaling analyses [J].