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Yes, the hierarchical clustering solution
can depend on case order:
"
Case order. If tied distances or similarities exist in
the input data or occur among updated clusters during joining, the resulting
cluster solution may depend on the order of cases in the file. You may
want to obtain several different solutions with cases sorted in different
random orders to verify the stability of a given solution.
"
The k-means solution can also depend
on case order:
"
Case and initial cluster center order. The default algorithm
for choosing initial cluster centers is not invariant to case ordering.
The Use running means option in the Iterate dialog box makes the resulting
solution potentially dependent on case order, regardless of how initial
cluster centers are chosen. If you are using either of these methods, you
may want to obtain several different solutions with cases sorted in different
random orders to verify the stability of a given solution. Specifying initial
cluster centers and not using the Use running means option will avoid issues
related to case order. However, ordering of the initial cluster centers
may affect the solution if there are tied distances from cases to cluster
centers. To assess the stability of a given solution, you can compare results
from analyses with different permutations of the initial center values.
"
Alex
Hello Friends,
I'm facing a problem, with Hierarchical clustering. If i change the order
of
the variable (keeping the same variables), I get different types of
solution.
E.g. if I run this syntax
CLUSTER v1 v2 v3 v4
/METHOD WARD
/MEASURE=SEUCLID
/PRINT SCHEDULE
/PLOT NONE
/SAVE CLUSTER(3).
or
CLUSTER v3 v4 v1 v2
/METHOD WARD
/MEASURE=SEUCLID
/PRINT SCHEDULE
/PLOT NONE
/SAVE CLUSTER(3).
..i get different cluster solution.
Is there some step I'm missing to do or is it typical and that's why we
run
a k-means after hierarchical segmentation to stabilize the outputs.
Any fast reply/ help would be much appreciated. Thanks.
Das.
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