Clustering Techniques (Skill test Solution)

Create an informative and visually appealing illustration that embodies the concepts of clustering techniques in data analysis, featuring charts, graphs, and data points interconnected to represent clustering algorithms like K-Means and hierarchical clustering.

Clustering Techniques Skill Test

Test your knowledge of clustering techniques with our engaging quiz designed for data enthusiasts and professionals alike. This quiz consists of 10 multiple-choice questions that cover various aspects of clustering, from K-Means to hierarchical clustering, ensuring you understand the key concepts and applications.

Whether you are a student, a data scientist, or simply interested in machine learning, this quiz is a great way to enhance your skills!

10 challenging questions
Multiple choice format
Learn as you assess your knowledge

10 Questions2 MinutesCreated by LearningData101

Q1. Movie Recommendation systems are an example of: Classification Clustering Reinforcement Learning Regression

A. 2 Only

B.1 and 2

C.1 and 3

D.2 and 3

Q4. Which of the following is the most appropriate strategy for data cleaning before performing clustering analysis, given less than desirable number of data points: 1.Capping and flouring of variables 2.Removal of outliers

A. 1 only

B. 2 only

C. 1 and 2

D. None of the above

Q6. After performing K-Means Clustering analysis on a dataset, you observed the following dendrogram. Which of the following conclusion can be drawn from the dendrogram?

A. There were 28 data points in clustering analysis

B. The best no. Of clusters for the analyzed data points is 4

C. The proximity function used is Average-link clustering

D. The above dendrogram interpretation is not possible for K-Means clustering analysis

Q7.What could be the possible reason(s) for producing two different dendrograms using agglomerative clustering algorithm for the same dataset?

Q9.Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2.Complete-link 3.Average-link

A. 1 and 2

B. 1 and 3

C. 2 and 3

D. 1, 2 and 3

Q10.Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0)} C3: {(5,5), (9,9)} What will be the Manhattan distance for observation (9, 9) from cluster centroid C1. In second iteration.

A. 10

B. 5*sqrt(2)

C. 13*sqrt(2)

D. None of these

Clustering Techniques (Skill test Solution)

Clustering Techniques Skill Test

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