There is no specific rule for this. In linear regression usually a rule is circulated requiring at least 10-20 cases per variable. Based on this rule, you should use a maximum of 5 variables, possibly extensible to 10 variables. But it all depends on the variability among your cases. If your 100 cases fall neatly within a few groups, and the variables are highly correlated among themselves, then you may use more variables and still get meaningful results (i.e. meaningful groups of cases). But if your cases are dispersed across all values and combinations of values of the various variables, you may as well form three clusters or thirty clusters, use four variables or forty variables... The general objective of a cluster analysis is to construct a few groups or clusters that are (a) internally homogeneous and (b) clearly distinct from other groups. If the groups are more or less equally distributed all over the variable-space, many will fall in the "gray area", more or less at an equal distance from various cluster centers, and thus attributing those cases to one cluster or to another would be essentially arbitrary, and all solutions would be highly unstable (changing even slightly the value of a case in some of the variables would throw it into a different cluster). In that kind of situation, larger samples (and larger cases/variables ratios) would be needed.
Hector
[此贴子已经被作者于2005-9-25 5:03:39编辑过]
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