Quiz on CLARA and CLARANS Algorithms

What does CLARA stand for?

• Clustering Large Applications for Randomized Sampling

• Clustering Large Applications

• Clustering Large Applications based on Randomized Search

• Clustering Large Algorithms

How many samples are typically taken in the CLARA algorithm?

• 3 samples

• 5 samples

• 10 samples

• 15 samples

What is the main disadvantage of CLARA?

• High computational cost

• Sensitivity to outliers

• Ineffective for small datasets

• Requires extensive parameter tuning

What is the primary method used by CLARANS to find the best set of medoids?

• Exhaustive search

• Dynamic programming

• Randomized search

• Greedy algorithm

What is a key advantage of CLARANS over CLARA?

• Better handling of noise

• Faster execution time

• Fewer parameters to tune

• Lower complexity

What does the cost in CLARANS represent?

• Total dissimilarity between objects and medoids

• Total number of clusters formed

• Total distance between objects

• Total time taken for clustering

What is the role of the confusion matrix in clustering?

• To calculate the average dissimilarity

• To visualize the clustering process

• To determine the number of clusters

• To represent the outcome of clustering on labeled data

Which of the following is a limitation of CLARA?

• It can handle large datasets

• It may miss the global structure of the dataset

• It requires no sampling

• It is less efficient than PAM

What is the main purpose of validity measures in clustering?

• To determine the optimal number of clusters

• To visualize clustering results

• To quantify the performance of clustering algorithms

• To improve computational efficiency

In CLARANS, what defines neighboring nodes?

• Nodes that represent the same partition

• Nodes that have no common elements

• Nodes with the same medoids

• Nodes with a cardinality of intersection equal to two