英语翻译1.IntroductionIt has long been recognized that not all v

英语翻译
1.Introduction
It has long been recognized that not all variables contribute equally to defining cluster structure (DeSarbo,Carroll,Clark,& Green,1984; De Soete,DeSarbo,& Carroll,1985; Donoghue,1990; Fowlkes,Gnanadesikan,& Kettenring,1988; Gnanadesikan,Kettenring,& Tsao,1995; Green,Carmone,& Kim,1990; Milligan,1989; van Buuren & Heiser,1989),and the inclusion of variables that do not define cluster structure (coined “masking variables” by Fowlkes & Mallows,1983) can actually degrade the ability of clustering procedures to effectively recover the true cluster structure (Milligan,1980; 1989).Recently,there has been a virtual well-spring of procedures attempting to determine the subset of variables that define true cluster structures.These procedures have been developed in both the context of model-based clustering (Dy & Brodley,2004; Law,Figueiredo,& Jain,2004; Raftery & Dean,2006) and non-model-based clustering (Brusco & Cradit,2001; Carmone,Kara,& Maxwell,1999; Friedman & Meulman,2004; Montanari& Lizzani,2001).Excluding the work of Brusco and Cradit (2001) and Carmone et al.(1999),when new procedures are introduced they are normally demonstrated on a few “choice”data sets and comprehensive comparisons are never provided.Unfortunately,introducing new variable selection procedures in this manner results in an entire collection of techniques where there are no definitive recommendations about when to use which procedure.The purpose of the current study is to provide an extensive comparison of recent variable selection techniques across a wide range of conditions.

1 .导言
一直以来,认识到,不是所有的变数平等作出贡献,确定团簇结构（ desarbo ,卡罗尔,克拉克,绿色,1984年;德soete ,desarbo ,＆卡罗尔,1985年;多诺霍,1990年;福尔克斯,格纳纳德西肯,＆ kettenring ,1988年;格纳纳德西肯,kettenring ,与曹兴诚,1995年;绿色,卡莫纳,与金,1990年;米利,1989年;范buuren ＆海舍尔称,1989年） ,并列入变数,不界定团簇结构（杜撰“掩蔽变数”福尔克斯＆ mallows ,1983 ）其实可以降解的能力,聚类程序,以有效收回真正的团簇结构（米利,1980 ; 1989年） .最近,有一个虚拟良好的春天程序,试图以确定子的变数,界定真正的团簇结构.这些程序已经制定了在这两个背景下基于模型的聚类（颐＆ brodley ,2004年;法,菲格雷多,＆ Jain公司,2004年; raftery ＆院长,2006年）和非基于模型的聚类（ brusco ＆ cradit ,2001年;卡莫纳,卡拉,与麦克斯韦,1999年;弗里德曼＆ meulman ,2004年; montanari ＆利扎尼,2001年） .不包括工作brusco和cradit （ 2001年）和卡莫纳等人（ 1999年） ,当新的程序,介绍了他们通常表现出对少数“生死抉择”数据集和全面的比较,从来没有提供.不幸的是,引入新的变量选择程序,在这种方式的结果在整个收集技术,有没有明确的建议,关于何时使用何种程序.的目的,目前的研究是为了提供一个广泛的比较,最近变量选择技术跨越多种条件.