Answer:
Explanation:
A. K- medoids algorithms is more robust and vigorous where there is noise and  outliers than K-means, this is because outliers have less effect on mediod or other extreme values than mean, although processing it is more expensive than when compared to K-means method.
B. Partitioning based clustering is done by both K- means and K- mediods. The advantage of the partitioning is that previous cluster steps can be overrule through iterative relocation compared with hierarchical method which does not permit adjustments once a split or merge is carried out. This flaw in the hierarchical method can affect the quality of the resulting clustering.
Based on the information given, it should be noted that k-medoids algorithms are more robust and vigorous.
It should be noted that both k-means and k-medoids algorithms can perform effective clustering. K-medoids are more robust and typically more expensive.
Furthermore, it can be deduced that the partitioning-based clustering can be done by both K- means and K- medoids. It ensures that previous cluster steps can be overruled.
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