# Min Hash Multiple Choice Questions and Answers (MCQs)

This set of Data Structures & Algorithms Multiple Choice Questions & Answers (MCQs) focuses on “Min Hash”.

1. Which of the following is defined as the ratio of total elements of intersection and union of two sets?
A) Rope Tree
B) Jaccard Coefficient Index
C) Tango Tree
D) MinHash Coefficient

Explanation: MinHash is a tool for quickly estimating the similarity of two sets. The Jaccard Coefficient is a measure of how close two sets are. The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets.

2. What is the value of the Jaccard index when the two sets are disjoint?
A) 1
B) 2
C) 3
D) 0

Explanation: MinHash is a tool for quickly estimating the similarity of two sets. The Jaccard Coefficient is a measure of how close two sets are. The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets. The value of the Jaccard index is zero for two disjoint sets.

3. When are the members of two sets more common relatively?
A) Jaccard Index is Closer to 1

B) Jaccard Index is Closer to 0
C) Jaccard Index is Closer to -1
D) Jaccard Index is Farther to 1

Explanation: The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets. The value of the Jaccard index is zero for two disjoint sets. When the Jaccard Index is closer to 1, members of two sets are more common.

4. What is the expected error for estimating the Jaccard index using MinHash scheme for k different hash functions?
A) O (log k!)
B) O (k!)
C) O (k2)
D) O (1/k½)

Explanation: The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets. The value of the Jaccard index is zero for two disjoint sets. For k different hash functions, the estimated error for estimating the Jaccard index using the MinHash scheme is O (1/k12).

5. How many hashes will be needed for calculating Jaccard index with an expected error less than or equal to 0.05?
A) 100
B) 200
C) 300
D) 400

Explanation: For k different hash functions, the estimated error for estimating the Jaccard index using the MinHash scheme is O (1/k12). Calculating the Jaccard index with an estimated error of less than or equal to 0.05 would require 400 hashes.

6. What is the expected error by the estimator Chernoff bound on the samples performed without replacement?
A) O (log k!)
B) O (k!)
C) O (k2)
D) O (1/k½)

Explanation: For k different hash functions, the estimated error for estimating the Jaccard index using the MinHash scheme is O (1/k12). On samples performed without substitution, the predicted error by the estimator Chernoff bound is O (1/k12).

7. What is the time required for single variant hashing to maintain the minimum hash queue?
A) O (log n!)
B) O (n!)
C) O (n2)
D) O (n)

Explanation: For k different hash functions, the estimated error for estimating the Jaccard index using the MinHash scheme is O (1/k12). The time it takes to manage the minimum hash queue using single variant hashing is O. (n).

8. How many bits are needed to specify the single permutation by min-wise independent family?
A) O (log n!)
B) O (n!)
C) Ω (n2)
D) Ω (n)

Explanation: The time it takes to manage the minimum hash queue using single variant hashing is O. (n). To define a single permutation by min-wise independent families, (n) bits are needed.

9. Is MinHash used as a tool for association rule learning.
A) True

B) False

Explanation: MinHash was created with the intention of removing redundant webpages from a search engine. However, Cohen used MinHash as a method for association rule learning in data mining in 2001.

10. Did Google conduct a large evaluation for comparing the performance by two technique MinHash and SimHash.
A) True

B) False

Explanation: MinHash was created with the intention of removing redundant webpages from a search engine. However, Cohen used MinHash as a method for association rule learning in data mining in 2001. The output of two techniques, MinHash and SimHash, was compared in a survey conducted by Google.

11. Which technique is used for finding similarity between two sets?
A) MinHash

B) Stack
C) Priority Queue
D) PAT Tree

Explanation: A technique known as MinHash or min-wise independent permutation scheme is used in computer science and data mining to find the similarity between two given sets. It aids in the estimation of similarities between two sets quickly.

12. Who invented the MinHash technique?
A) Weiner
B) Samuel F. B. Morse
C) Friedrich Clemens Gerke
D) Andrei Broder

Explanation: A technique known as MinHash or min-wise independent permutation scheme is used in computer science and data mining to find the similarity between two given sets. It aids in the estimation of similarities between two sets quickly. Andrei Broder came up with the idea in 1997.

13. Which technique was firstly used to remove duplicate web pages from search results in AltaVista search engine?
A) MinHash

B) Stack
C) Priority Queue
D) PAT Tree

Explanation: A technique known as MinHash or min-wise independent permutation scheme is used in computer science and data mining to find the similarity between two given sets. It aids in the estimation of similarities between two sets quickly. It is used by the AltaVista search engine to exclude duplicate web pages from search results.

14. Which technique was firstly used clustering documents using the similarity of two words or strings?
A) MinHash

B) Stack
C) Priority Queue
D) PAT Tree

Explanation: A technique known as MinHash or min-wise independent permutation scheme is used in computer science and data mining to find the similarity between two given sets. It aids in the estimation of similarities between two sets in a timely manner. It’s used to group documents based on how similar two words or strings are.

15. Which indicator is used for similarity between two sets?
A) Rope Tree
B) Jaccard Coefficient
C) Tango Tree
D) MinHash Coefficient

Explanation: A technique known as MinHash or min-wise independent permutation scheme is used in computer science and data mining to find the similarity between two given sets. It aids in the estimation of similarities between two sets in a timely manner. The Jaccard Coefficient is a measure of how close two sets are.

MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a method for determining how close two sets are quickly. Andrei Broder (1997) invented the scheme, which was first used in the AltaVista search engine to find duplicate web pages and exclude them from search results. It’s also been used to solve large-scale clustering problems, such as grouping documents based on the similarity of their word sets. To use the MinHash scheme as defined above, you’ll need the hash function h to create a random permutation on n elements, where n is the total number of distinct elements in the union of all the sets to compare.