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
收藏
页码：221 / 229
页数：9
相关论文
共 25 条
  • [1] [Anonymous], 2005, Wiley series in probability and statistics
  • [2] [Anonymous], 1984, Theory and Application of Correspondence Analysis
  • [3] Arabie P., 1994, ADV METHODS MARKETIN, P160
  • [4] Bezdek J. C., 1981, Pattern recognition with fuzzy objective function algorithms
  • [5] Iterative factor clustering of binary data
    D'Enza, Alfonso Iodice
    Palumbo, Francesco
    [J]. COMPUTATIONAL STATISTICS, 2013, 28 (02) : 789 - 807
  • [6] MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM
    DEMPSTER, AP
    LAIRD, NM
    RUBIN, DB
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01): : 1 - 38
  • [7] A penalized latent class model for ordinal data
    Desantis, Stacia M.
    Houseman, E. Andres
    Coull, Brent A.
    Stemmer-Rachamimov, Anat
    Betensky, Rebecca A.
    [J]. BIOSTATISTICS, 2008, 9 (02) : 249 - 262
  • [8] Everitt B., STAT PROBAB LETT
  • [9] Everitt BS., 1990, J APPL STAT, V17, P283
  • [10] Model-based clustering, discriminant analysis, and density estimation
    Fraley, C
    Raftery, AE
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2002, 97 (458) : 611 - 631
123