Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches

被引：2

作者：

Ranalli, Monia ^{[1
]}

Rocci, Roberto ^{[2
]}

机构：

[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA

[2] Univ Roma Tor Vergata, Dipartimento IGF, Rome, Italy

来源：

ADVANCES IN STATISTICAL MODELS FOR DATA ANALYSIS | 2015年

关键词：

EM algorithm; Finite mixture models; k-means; Ordinal data; Pairwise likelihood; MODEL;

D O I：

10.1007/978-3-319-17377-1_23

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The literature on cluster analysis has a long and rich history in several different fields. In this paper, we provide an overview of the more well-known clustering methods frequently used to analyse ordinal data. We summarize and compare their main features discussing some key issues. Finally, an example of application to real data is illustrated comparing and discussing clustering performances of different methods.

引用

页码：221 / 229

页数：9

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