Test Series - Data Structure

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Test Number 106/115

Q: Which technique is used for finding similarity between two sets?
A. MinHash
B. Stack
C. Priority Queue
D. PAT Tree
Solution: In computer science as well as data mining, to find the similarity between two given sets, a technique called MinHash or min-wise independent permutation scheme is used. It helps in the quick estimation of the similarity between two sets.
Q: Who invented the MinHash technique?
A. Weiner
B. Samuel F. B. Morse
C. Friedrich Clemens Gerke
D. Andrei Broder
Solution: In computer science as well as data mining, to find the similarity between two given sets, a technique called MinHash or min-wise independent permutation scheme is used. It helps in the quick estimation of the similarity between two sets. It was invented by Andrei Broder in 1997.
Q: 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
Solution: In computer science as well as data mining, to find the similarity between two given sets, a technique called MinHash or min-wise independent permutation scheme is used. It helps in the quick estimation of the similarity between two sets. It is used in removing duplicate web pages from search results in AltaVista search engine.
Q: 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 
Solution: In computer science as well as data mining, to find the similarity between two given sets, a technique called MinHash or min-wise independent permutation scheme is used. It helps in the quick estimation of similarity between two sets. It is used in clustering documents using the similarity of two words or strings.
Q: Which indicator is used for similarity between two sets?
A. Rope Tree
B. Jaccard Coefficient
C. Tango Tree
D. MinHash Coefficient
Solution: In computer science as well as data mining, to find the similarity between two given sets, a technique called MinHash or min-wise independent permutation scheme is used. It helps in the quick estimation of similarity between two sets. Jaccard Coefficient is used for similarity between two sets.
Q: 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
Solution: MinHash helps in the quick estimation of similarity between two sets. Jaccard Coefficient is used for similarity between two sets. Jaccard Coefficient Index is defined as the ratio of total elements of intersection and union of two sets.
Q: What is the value of the Jaccard index when the two sets are disjoint?
A. 1
B. 2
C. 3
D. 0
Solution: MinHash helps in the quick estimation of similarity between two sets. Jaccard Coefficient is used for the similarity between two sets. Jaccard Coefficient Index is defined as the ratio of total elements of intersection and union of two sets. For two disjoint sets, the value of the Jaccard index is zero.
Q: 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
Solution: Jaccard Coefficient Index is defined as the ratio of total elements of intersection and union of two sets. For two disjoint sets, the value of the Jaccard index is zero. The members of two set more common relatively when the Jaccard Index is Closer to 1.
Q: 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)
Solution: Jaccard Coefficient Index is defined as the ratio of total elements of intersection and union of two sets. For two disjoint sets, the value of the Jaccard index is zero. The expected error for estimating the Jaccard index using MinHash scheme for k different hash functions is O (1/k½).
Q: 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
Solution: The expected error for estimating the Jaccard index using MinHash scheme for k different hash functions is O (1/k½). 400 hashes will be needed for calculating Jaccard index with an expected error less than or equal to 0.05.

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