Total SSE is the sum of the SSE for each separate attribute. What does it mean if the SSE for one variable is low for all clusters? Low for just one cluster? High for all clusters? High for just one cluster? How could you use the per variable SSE information to improve your clustering?

Question

What will be an ideal response?

Answer 1

(a) If the SSE of one attribute is low for all clusters, then the variable is
essentially a constant and of little use in dividing the data into groups.
(b) if the SSE of one attribute is relatively low for just one cluster, then
this attribute helps define the cluster.
(c) If the SSE of an attribute is relatively high for all clusters, then it could
well mean that the attribute is noise.
(d) If the SSE of an attribute is relatively high for one cluster, then it is
at odds with the information provided by the attributes with low SSE
that define the cluster. It could merely be the case that the clusters
defined by this attribute are different from those defined by the other
attributes, but in any case, it means that this attribute does not help
define the cluster.
(e) The idea is to eliminate attributes that have poor distinguishing power
between clusters, i.e., low or high SSE for all clusters, since they are
useless for clustering. Note that attributes with high SSE for all clusters
are particularly troublesome if they have a relatively high SSE with
respect to other attributes (perhaps because of their scale) since they
introduce a lot of noise into the computation of the overall SSE.