Inability to handle categorical data.

Difficulty in detemining the number of clusters.

Efficiency on large datasets and high$-$dimensional data.

Sensitivity to initial conditions.

Unsuitable for clusters of arbitrary shapes.

Options:

A$.1,3$ and $4$

B$.1\mathrm{.}2$ and $3$

C$.1,2$ and $4$

D$.$ All the above

Q$3.$ Which of the following clustering algorithms suffers from

the problem of convergence at local optima?

K$-$Means clustering algorithm.

Hierarchy clustering algorithm.

Expectation$-$Maximization clustering algorithm.

Gaussian Mixture Model clustering algorithm.

Options:

A$.1$ only

B$.2$ and $3$

C$.3$ and $4$

D$.1,3$ and $4$