Question

Which one is NOT-TRUE about K-means clustering
rely on updating the means valued of each attribute
no need to perform outlier detection and data normalization as required preprocessing step.
belong to "Modeling" step of CRISP-DM process
does not generate a dendrogram as a result
No answers

Answer 1

Answer: For K-means, data Normalization is not always required (however it always improves clustering), but it rarely hurts. Some examples: K-means: K-means clustering is "isotropic" in all directions of space and therefore tends to produce more or less round (rather than elongated) clusters.