标题: 主成分分析和按变量进行聚类分析 [打印本页] 作者: shiyiming 时间: 2004-1-18 18:46 标题: 主成分分析和按变量进行聚类分析 请问主成分分析和按变量进行聚类分析，这两种分析之间的区别是什么？作者: shiyiming 时间: 2004-2-1 17:49
Hey, it is not a fair question. The principal component analysis and classification are quite different from each other, but you have to do your homework. Basically, a principal component is a linear combination of several factors, and the impact is measured by the characteristic values associated with each combination; while the classification deals with one or several individual factors without considering the linear combinations. I took multivariate analysis long time ago, this statement came from my memory, I am not 100 percent sure.作者: shiyiming 时间: 2004-3-23 18:55 标题: 主成分分析: 主成分分析:
对多个变量进行降纬,把几个变量线形组合为一个变量,同时使丧失的信息最少,
对于一些有多重共线性的模型很有帮助(把相关性很高的变量组合成一个变量,避免
多重共线性),
主成分分析只是通常的变量变换，而且只是原变量的线形组合，分量的个数与变量的个数相同，一般降维的方法基本用因子分析，而不是主成分分析。作者: shiyiming 时间: 2004-3-25 18:52
hey, r u one of our team? if yes, u should ask me in person, it is easier for me to explain to you in details.
any way, in summary
For factor based clustering
* Pros: free of multi collinearity
* Cons: hard to implement, because more variables in final model and hard to explain the factor meaning to business. Sometimes may come out meaningless results
For variable based clustering
*Pros: easy to understand and easy to implement
*Cons: may suffer from multi collinearity
Personally, I will not say factor based clustering is better or variable based clustering is better. The quality of the analysis depends on a lot of factors. Among these factors, techniques comprise for only a very small part.作者: shiyiming 时间: 2004-5-1 07:54 标题: Re: 主成分分析: [quote="amiao":81e8d]主成分分析只是通常的变量变换，而且只是原变量的线形组合，分量的个数与变量的个数相同，一般降维的方法基本用因子分析，而不是主成分分析。[/quote:81e8d]
这种说法第一次听说，不过细想下，确实是这样的！不过进行因子分析之前还要检验是否符合进行因子分析的条件，不符合的话只有进行主成分分析了！