CsCI$4$I$5/657$

Final Fenm uarr

Name:

t$.$ Multiple Choice Questions $(3$pts$/$ea total $3**13-39pts$

Supervised learning and unsupervisied elustering both require at least one

a$.$ hidden attributo.

b$.$ output attribute.

c$.$ input attribute.

d$.$ categorical attribute, among a set of attributes.

a$.$ decision tree

be association rules

c$.$ K$-$Means algorithm

d$.$ genetic learning

The K$-$Means algorithm terminates when

a$.$ a user$-$defined minimum value for the summation of squared error differences between instances and their corresponding cluster center is seen.

b$.$ the cluster centers for the current iteration are identical to the cluster centers for the previous iteration.

c$.$ the number of instances in each cluster for the current iteration is identical to the number of instances in each cluster of the previous iteration.

d$.$ the number of clusters formed for the current iteration is identical to the number of clusters formed in the previous iteration.

Which of following are interestingness measures for association rules?

a accuracy

b$.$ recall

c$.$ compactness

d$.$ lift

A frequent set is a set. if it is a frequent set and no superset of this is a frequent

a$.$ Border set

b$.$ Minimal frequent set

c$.$ Maximal frequent set

d$.$ None of the above