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PSYCHOMETRIKA—VOL. 73, NO. 1, 125–144
MARCH 2008
DOI: 10.1007/S11336-007-9019-Y
SELECTION OF VARIABLES IN CLUSTER ANALYSIS:AN EMPIRICAL COMPARISON OF EIGHT PROCEDURES
DOUGLAS STEINLEY
UNIVERSITY OF MISSOURI-COLUMBIA
MICHAEL J. BRUSCO
FLORIDA STATE UNIVERSITY
Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with fi
MARCH 2008
DOI: 10.1007/S11336-007-9019-Y
SELECTION OF VARIABLES IN CLUSTER ANALYSIS:AN EMPIRICAL COMPARISON OF EIGHT PROCEDURES
DOUGLAS STEINLEY
UNIVERSITY OF MISSOURI-COLUMBIA
MICHAEL J. BRUSCO
FLORIDA STATE UNIVERSITY
Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with fi
psychometrika -卷。 73 ，没有。 1 ， 125-144
2008年3月
土井： 10.1007/s11336-007-9019-y
变量选择在聚类分析：一个实证比较8程序
道格拉斯steinley
密苏里大学哥伦比亚
菲利普莫里斯brusco
佛罗里达州立大学
8个不同的变量选择技术，基于模型和非基于模型的聚类评估，跨越多种团簇结构。这表明几种方法有困难时，非翔实的变数（即随机噪声）都包含在模型。此外，分布的随机噪声大大影响的表现，几乎所有的变量选择程序。整体而言，一个变量选择技术的基础上，方差，以远程加权程序，再加上最大的跌幅在内部联网平方错误表现是最好的。在另一方面，变量选择所采用的方法，结合有限混合模型的表现最差。关键词：聚类分析，变量选择。