Clustering Procedure in SAS Lei Department of Electrical and Computer Engineering University of Victoria, Canada Introduction coordinate data, Euclidean distances are computed from differences between coordinate values as the use The clustering is to group data into of differences has several important different groups by computing the consequences: distance between the datasets. The distance between the datasets is For differences to be valid, defined the closeness of the datasets. the variables must have an In machine learning, the clustering is interval or stronger scale of also called unsupervised learning, in measurements. Ordinal or which the datasets used do not have ranked data are generally labeled classes or groups. The not appropriate for cluster methods applied to compute the analysis. distance vary from some simple For Euclidean distances to measures to very complicate ones be comparable, equal depending on specific research differences should have purposes and the only difference is equal practical importance. how to compute the distance between Variables with large two clusters. variances tend to have more effect on the resulting The general process of the clusters than variables with clustering is that each observation or small variances. data record begins in a cluster by itself; then, the two closest clusters are The eleven clustering methods merged to form a new one to replace supported in SAS are: average linkage, the two old ones; continuing to repeat the centroid method, complete the merging the two closest clusters linkage, density linkage (including until only one cluster is left. Wong’s hybrid and kth-neighbor methods), maximum likelihood for The CLUSTER Procedure in SAS mixtures of spherical multivariate hierarchically clusters the data normal distributions with equal records or observations in a SAS data variances but possibly unequal mixing set by using one of the eleven proportions, the flexible-beta method, methods. The data can be coordinates McQuitty’s similarity analysis, the or distances. If the data re coordinates, median method, single linkage, PROC CLUSTER computes (possibly density linkage, and Ward’s squared) Euclidean distances. For
Clustering Procedure in SAS Lei Department of Electrical and Computer Engineering University of Victoria, Canada Introduction The clustering is to group data into different groups by computing the distance between the datasets. The distance between the datasets is defined the closeness of the datasets. In machine learning, the clustering is also called unsupervised learning, in which the datasets used do not have labeled classes or groups. The methods applied to compute the distance vary from some simple measures to very complicate ones depending on specific research purposes and the only difference is how to compute the distance between two clusters. The general process of the clustering is that each observation or data record begins in a cluster by itself; then, the two closest clusters are merged to form a new one to replace the two old ones; continuing to repeat the merging the two closest clusters until only one cluster is left. The CLUSTER Procedure in SAS hierarchically clusters the data records or observations in a SAS data set by using one of the eleven methods. The data can be coordinates or distances. If the data re coordinates, PROC CLUSTER computes (possibly squared) Euclidean distances. For coordinate data, Euclidean distances are computed from differences between coordinate values as the use of differences has several important consequences: For differences to be valid, the variables must have an interval or stronger scale of measurements. Ordinal or ranked data are generally not appropriate for cluster analysis. For Euclidean distances to be comparable, equal differences should have equal practical importance. Variables with large variances tend to have more effect on the resulting clusters than variables with small variances. The eleven clustering methods supported in SAS are: average linkage, the centroid method, complete linkage, density linkage (including Wong’s hybrid and kth-neighbor methods), maximum likelihood for mixtures of spherical multivariate normal distributions with equal variances but possibly unequal mixing proportions, the flexible-beta method, McQuitty’s similarity analysis, the median method, single linkage, density linkage, and Ward’s
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