The CLUSTER Procedure
Algorithms
Anderberg (1973) describes three algorithms for implementing agglomerative hierarchical clustering: stored data, stored distance, and sorted distance. The algorithms used by PROC CLUSTER for each method are indicated in Table 31.3. For METHOD=AVERAGE, METHOD=CENTROID, or METHOD=WARD, either the stored data or the stored distance algorithm can be used. For these methods, if the data are distances or if you specify the NOSQUARE option, the stored distance algorithm is used; otherwise, the stored data algorithm is used.
Table 31.3: Three Algorithms for Implementing Agglomerative Hierarchical Clustering
|
Algorithm |
|||
|---|---|---|---|
|
Clustering |
Stored |
Stored |
Sorted |
|
Method |
Data |
Distance |
Distance |
|
AVERAGE |
x |
x |
|
|
CENTROID |
x |
x |
|
|
COMPLETE |
x |
||
|
DENSITY |
x |
||
|
EML |
x |
||
|
FLEXIBLE |
x |
||
|
MCQUITTY |
x |
||
|
MEDIAN |
x |
||
|
SINGLE |
x |
||
|
TWOSTAGE |
x |
||
|
WARD |
x |
x |
|
|
Note: All of the hierarchical methods accept coordinate data. Methods that require stored or sorted distances automatically calculate distances from the coordinates. |
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