1. 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

2. Clustering is a type of ______ learning technique.

A . Supervised

B . Semi-supervised

C . Unsupervised

D . Reinforcement

Answer

3. The main goal of clustering is to identify ______ in the data.

A . Noise

B . Patterns or relationships

C . Outliers

D . Dependencies

Answer

4. In clustering, data points within the same cluster are ______.

A . Dissimilar

B . Similar

C . Random

D . Opposite

Answer

5. Which of the following is a partitioning-based clustering method?

A . K-Means

B . DBSCAN

C . Hierarchical

D . Grid-based

Answer

6. In the K-Means algorithm, each cluster is represented by a ______.

A . Node

B . Centroid

C . Point

D . Median

Answer

7. K-Means clustering aims to minimize the sum of ______ between data points and their clusters.

A . Angles

B . Distances

C . Correlations

D . Weights

Answer

8. The K-Medoids algorithm is also known as ______.

A . PAM (Partitioning Around Medoids)

B . LAM (Local Assignment of Medoids)

C . CAM (Cluster Assignment Method)

D . RAM (Random Assignment Method)

Answer

9. In K-Medoids, the cluster center is always a ______.

A . Mean

B . Data point

C . Hypothetical point

D . Zero point

Answer

10. Which of the following clustering methods uses a tree-like structure?

A . Grid-based

B . Hierarchical

C . Density-based

D . Partitioning

Answer

11. The tree-like structure formed in hierarchical clustering is called a ______.

A . Graph

B . Dendrogram

C . Lattice

D . Chain

Answer

12. Agglomerative clustering follows a ______ approach.

A . Top-down

B . Bottom-up

C . Random

D . Sequential

Answer

13. Divisive hierarchical clustering follows a ______ approach.

A . Top-down

B . Bottom-up

C . Random

D . Iterative

Answer

14. DBSCAN algorithm groups data based on ______.

A . Density

B . Similarity

C . Probability

D . Gradient

Answer

15. The parameter "ε (epsilon)" in DBSCAN defines ______.

A . Number of clusters

B . Distance threshold (radius)

C . Learning rate

D . Number of iterations

Answer

16. In DBSCAN, MinPts parameter defines ______.

A . Minimum distance

B . Minimum number of neighbors

C . Maximum distance

D . Maximum density

Answer

17. In DBSCAN, points that do not belong to any cluster are called ______.

A . Core points

B . Border points

C . Noise points

D . Dense points

Answer

18. Outlier analysis helps in identifying data points that ______.

A . Follow a trend

B . Differ from the majority

C . Have maximum density

D . Are similar to others

Answer

19. Which of the following is a density-based clustering algorithm?

A . K-Means

B . K-Medoids

C . DBSCAN

D . Hierarchical

Answer

20. The technique used to find the distance between two clusters in hierarchical clustering is called ______.

A . Aggregation method

B . Linkage method

C . Proximity measure

D . Distance method

Answer

Fill in the Blanks

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21. Clustering groups similar data points together based on their ______.

Answer

22. K-Means is a ______-based clustering algorithm.

Answer

23. In K-Means, the number of clusters (k) must be ______ before execution.

Answer

24. The K-Medoids algorithm is designed to handle ______ data.

Answer

25. The hierarchical clustering output is represented using a ______.

Answer

26. The two main types of hierarchical clustering are ______ and ______.

Answer

27. DBSCAN uses two parameters: ______ and ______.

Answer

28. In DBSCAN, a point with at least MinPts within its epsilon neighborhood is called a ______ point.

Answer

29. Data points that deviate significantly from the dataset are called ______.

Answer

30. In distance-based clustering, the most commonly used distance measure is ______ distance.

Answer

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