Sol one of the major strengths of the k medoids

Sol: One of the major strengths of the K-medoids algorithm is that it’s more robust
when there is either noise or outliers. K-medoids algorithm is typically influenced
to a minimum extent due to outliers. They are also not influenced by the values
which are far too extreme than the mean. The disadvantage of K-medoids
algorithm over k means method is that it’s not cost effective.
(b) Illustrate the strength and weakness of these schemes in comparison with a
hierarchical clustering scheme (e.g., AGNES).

the number of clusters present in the database before performing the algorithm.
Where as in hierarchical methods, the algorithm calculates the number of clusters
in the database automatically. They might have to struggle during scaling because
with each decision of merging and splitting, the algorithm needs to examine and
evaluate an appropriate number of data objects or clusters. Where as in hierarchical
methods, we can integrate clustering approaches to improve the efficiency and
accuracy of the clusters. We can consider BIRCH, ROCK, and Chameleon as
examples.
Submitted by
Sphoorthy Masa
[email protected]
KSU ID : 811024145