Assignment 9 Computing Proximities

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Arizona State University *

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511

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Computer Science

Date

Apr 3, 2024

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13

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Uploaded by AgentQuetzal3025

Assignment 9: Computing Proximities Prasad Srinivas IFT 511: Analyzing Big Data Professor: Asmaa Elbadrawy Tuesday and Thursday (12:00 PM – 1:15 PM) October 8 th , 2023
Similarities Between Binary Data Point 2
3
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4
3. User 3 shows a resemblance, to User 1 than User 2 as evidenced by their Simple Matching Coefficients (SMCs). The SMC between User 1 and User 3 is 0.8 while the SMC between User 1 and User 2 is 0.4. Therefore, according to the Simple Matching Coefficient, we can conclude that User 3 bears similarity, to User 1. 5
6
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4.c. Using the Jaccard coefficient, it's determined that User 3 has a score of 0.6667, whereas User 2 has a score of 0.25. Therefore, based on these values, User 3 is more similar to User 1. 5. where there are movies available, but people usually only watch a small fraction of them it's important to focus on the similarities, between users based on the movies they have watched. Disregarding the movies, they haven't seen is crucial for accuracy. That's why the Jaccard coefficient is the method for assessing user similarities because it accurately measures how much two users share in terms of the movies, they have both watched while disregarding any information about movies they haven't seen. This approach ensures that user similarity is determined based on their shared movie preferences, which makes it the suitable choice, for this situation. Proximities Between Continuous Data Points 7
8
3. Considering the values from the above answers points  1 and 2, point 2 is more similar to point 1 because the cosine value of points 1 and 2 is greater than the cosine value of point 1 and 3. 9
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10
11
12
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6. Computing the Euclidean values, we have obtained the following: Euclidean distance (point 1 and 2) = 1.732 Euclidean distance (point 1 and 3) = 3.60 Thus, based on the above Euclidean values, point 3 is farther than from point 1 compared to point 2. 13

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