INTRODUCTION TO MACHINE LEARNING Week 10

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Session: JAN-APR 2023

Course Name: Introduction to Machine Learning

Course Link: Click Here

These are Introduction to Machine Learning Week 10 Assignment 10 Answers

Q1.Consider the following one dimensional data set: 12, 22, 2, 3, 33, 27, 5, 16, 6, 31, 20, 37, 8 and 18. Given k=3 and initial cluster centers to be 5, 6 and 31, what are the final cluster centres obtained on applying the k-means algorithm?
a. 5, 18, 30
b. 5, 18, 32
c. 6, 19, 32
d. 4.8, 17.6, 32
e. None of the above

Answer: d. 4.8, 17.6, 32

Q2. For the previous question, in how many iterations will the k-means algorithm converge?
a. 2
b. 3
c. 4
d. 6
e. 7

Answer: c. 4

These are Introduction to Machine Learning Week 10 Assignment 10 Answers

Q3. In the lecture on the BIRCH algorithm, it is stated that using the number of points N, sum of points SUM and sum of squared points SS, we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the centroid of the combined cluster? (In terms of N,SUM and SS of both the clusters)
a. SUMA+SUMB
b. SUMA/NA+SUMB/NB
c. SUMA+SUMB/NA+NB
d. SSA+SSB/NA+NB

Answer: c. SUMA+SUMB/NA+NB

Q4. What assumption does the CURE clustering algorithm make with regards to the shape of the clusters?
a. No assumption
b. Spherical
c. Elliptical

Answer: a. No assumption

These are Introduction to Machine Learning Week 10 Assignment 10 Answers

Q5. What would be the effect of increasing MinPts in DBSCAN while retaining the same Eps parameter? (Note that more than one statement may be correct)
a. Increase in the sizes of individual clusters
b. Decrease in the sizes of individual clusters
c. Increase in the number of clusters
d. Decrease in the number of clusters

Answer: b, c

For the next question, kindly download the dataset – DS1. The first two columns in the dataset correspond to the co-ordinates of each data point. The third column corresponds two the actual cluster label.
DS1: Click here

Q6. Visualize the dataset DS1. Which of the following algorithms will be able to recover the true clusters (first check by visual inspection and then write code to see if the result matches to what you expected).
a. K-means clustering
b. Single link hierarchical clustering
c. Complete link hierarchical clustering
d. Average link hierarchical clustering

Answer: b. Single link hierarchical clustering

These are Introduction to Machine Learning Week 10 Assignment 10 Answers

Q7. Consider the similarity matrix given below: Which of the following shows the hierarchy of clusters created by the single link clustering algorithm.

Answer: b

Q8. For the similarity matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.