METHOD=WARD if the data set distance between two clusters. It is contains both of the following: strongly biased toward producing clusters with roughly equal diameters, a variable giving the number of and it can be severely distorted by original observations in each moderate outliers. cluster. a variable giving the root mean Density linkage (METHOD = squared standard deviation of DENSITY) is referred to a class of each other. clustering methods that use nonparametric probability density VAR statement lists numeric estimates. It consists of two steps: variables to be used in the cluster analysis. If it is omitted, all numeric a new dissimilarity measure variables not listed in other based on density estimates and statements are used. adjacencies is computed. a single linkage cluster analysis is performed. Clustering Methods There are three types of density There are eleven clustering linkage: the kth-nearest-neighbor methods provided in SAS. The method method, the uniform-kernel method of average linkage (METHOD = and Wong’s hybrid method. AVERAGE or AVG) computes the average distance between pairs of EML method (METHOD = EML) observations, one in each other. clusters to maximize the Average linkage tends to join clusters likelihood at each level of the with small variances, and it is slightly hierarchy under the following biased toward producing clusters with assumptions: the same variance. multivariate normal mixture In the centroid method (METHOD equal spherical covariance = CENTROID), the distance between matrices two clusters is defined as the unequal sampling probabilities (squared) Euclidean distance between their centroids or means. This method This method is similar to Ward’s is more robust to outliers than most minimum-variance method but other hierarchical methods but in removes the bias toward other respects might not perform as clusters. well as Ward’s method or average linkage. The distance computed by the method (METHOD = FLEXIBLE) Complete linkage method is presented as (METHOD = COMPLETE) use the maximum distance between an observation in one cluster and an observation in the cluster as the
METHOD=WARD if the data set contains both of the following: a variable giving the number of original observations in each cluster. a variable giving the root mean squared standard deviation of each other. VAR statement lists numeric variables to be used in the cluster analysis. If it is omitted, all numeric variables not listed in other statements are used. Clustering Methods There are eleven clustering methods provided in SAS. The method of average linkage (METHOD = AVERAGE or AVG) computes the average distance between pairs of observations, one in each other. Average linkage tends to join clusters with small variances, and it is slightly biased toward producing clusters with the same variance. In the centroid method (METHOD = CENTROID), the distance between two clusters is defined as the (squared) Euclidean distance between their centroids or means. This method is more robust to outliers than most other hierarchical methods but in other respects might not perform as well as Ward’s method or average linkage. Complete linkage method (METHOD = COMPLETE) use the maximum distance between an observation in one cluster and an observation in the cluster as the distance between two clusters. It is strongly biased toward producing clusters with roughly equal diameters, and it can be severely distorted by moderate outliers. Density linkage (METHOD = DENSITY) is referred to a class of clustering methods that use nonparametric probability density estimates. It consists of two steps: a new dissimilarity measure based on density estimates and adjacencies is computed. a single linkage cluster analysis is performed. There are three types of density linkage: the kth-nearest-neighbor method, the uniform-kernel method and Wong’s hybrid method. EML method (METHOD = EML) clusters to maximize the likelihood at each level of the hierarchy under the following assumptions: multivariate normal mixture equal spherical covariance matrices unequal sampling probabilities This method is similar to Ward’s minimum-variance method but removes the bias toward clusters. The distance computed by the method (METHOD = FLEXIBLE) is presented as
Clustering%20Procedure%20in%20SAS.docx