1. k-means is one of the simplest _______________ learning algorithms.                    

A . Supervised

B . Unsupervised

C . Both

D . None

Answer

2. Clustering is a_________________________________

A . Group of similar objects that differ significantly from other objects

B . Operations on a data to transform data in order to prepare it for a data mining algorithm

C . Symbolic representation of facts from which information can potentially be extracted

D . None

Answer

3. ____________________ method is used for partitioning the clusters based on the medoid objects.

A . k-means

B . k-medoid

C . Divisive

D . DBSCAN

Answer

4. ______________ hierarchical clustering algorithm initiates with clusters and integrates the two closest clusters until only one cluster remains.

A . DBSCAN

B . Agglomerative

C . k-means

D . Divisive

Answer

5. In ___________________ algorithm, each cluster is represented by the mean value of the objects in the cluster.

A . k-means

B . k-medoid

C . DBSCAN

D . Hierarchal

Answer

6. Which of the following is not a requirement of clustering?

A . Scaling of data

B . Large dimensionality

C . Divisive clustering

D . Arbitrary shaped cluster detection

Answer

7. _________method works by grouping data objects into a tree of clusters

A . Partition based clustering

B . Grid based clustering

C . Density based clustering

D . Hierarchical based clustering

Answer

8. A distance between different cluster is measured through___________

A . Linkage metric

B . Relation metric

C . Weight metric

D . None of the above

Answer

9. ____________ is a technique of combining a group of physical objects into classes of homogeneous objects.

A . Clustering

B . Grapping

C . Bisecting

D . Optimizing

Answer

10.) K-medoids  clustering methods uses one representative________ per cluster to represent the clusters.

A . Mean

B . Median

C . Object

D . Space

Answer

11.) The occurrences of_________ is inevitable when Euclidean distance criterion is employed.

A . Outlier

B . Empty cluster

C . Centroids

D . DBSCAN

Answer

Fill in the Blanks

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12. Cluster contains a set of_______ data objects.

Answer

13. Huge datasets are partitioned into groups based on___________.

Answer

14. _____________________________methods can find arbitrary shaped clusters.

Answer

15. DBSCAN stands for_____________________________________________________.

Answer

16. Outliers are different from noisy data (True/False)

Answer

17. _______________________________ is a "bottom-up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hie

Answer

18. ________________________ distance measure is used to find similarity among data points in k-means algorithm.

Answer

19. _________________ algorithm is efficient for clustering even if the dataset contains outliers.

Answer

20. _______________________________ is a "top-down" approach in hierarchical clustering m

Answer

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