文档介绍：Chapter19 Clustering Analysis
Content
Similarity coefficient
Hierarchical clustering analysis
Dynamic clustering analysis
Ordered sample clustering analysis
Discriminant Analysis : having known with certainty e from two or more populations, it’s a method to acquire the discriminate model that will allocate further individuals to the correct population. Clustering Analysis: a statistic method for grouping objects of random kind into respective categories. It’s used when there’s no priori hypotheses, but trying to find the most appropriate sorting method resorting to mathematical statistics and some collected information. It has e the first selected means to uncover great capacity of ic messages. Both are methods of multivariate statistics to study classification.
Clustering analysis is a method of exploring statistical analysis. It can be classified into two major species according to its aims.
For example, m refers to the number of variables(. indexes) while n refers to that of cases(. samples) ,you can do as follows:
(1) R-type clustering: also called index clustering. The method to sort the m kinds of indexes, aiming at lowering the dimension of indexes and choosing typical ones.
(2)Q-type clustering: also called sample clustering. The method to sort the n kinds of samples to find monness among them.
The most important thing for both R-type clustering and Q-type clustering is the definition of similarity, that is how to quantify similarity. The first step of clustering is to define the metric similarity between two indexes or two samples- similarity coefficient
§ 1 similarity coefficient
1 similarity coefficient of R-type clustering
Suppose there are m kinds of variables: X1,X2,…,Xm. R-type clustering usually use the absolute value of simple correlation coefficient to define the similarity coefficient among variables:
The two variables tend to be more similar when the
absolute value increases.
Similarly, Spearman rank correlation coeffic