
Data Mining and Warehousing
Flashcard
•
Engineering
•
University
•
Practice Problem
•
Hard
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20 questions
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1.
FLASHCARD QUESTION
Front
Which of the following distance metrics is sensitive to outliers? a. Euclidean distance, b. Manhattan distance, c. Chebyshev distance, d. All of the above
Back
Euclidean distance
2.
FLASHCARD QUESTION
Front
Which of the following distance metrics is most appropriate for text data? a. Euclidean distance, b. Cosine distance, c. Manhattan distance, d. Chebyshev distance
Back
Cosine distance
3.
FLASHCARD QUESTION
Front
3. __________techniques can be used to reduce the number of values for a given continuous attribute, by dividing the range of the attribute into intervals.
Back
Discretization
4.
FLASHCARD QUESTION
Front
Minkowski Distance is a generalization of Euclidean Distance and it is equivalent to Euclidean Distance when r is equal to
Back
2
5.
FLASHCARD QUESTION
Front
The dissimilarity of numerical data is calculated by using: a. Minkowski distance, b. Euclidean distance, c. Manhatten distance, d. All of above
Back
All of above
6.
FLASHCARD QUESTION
Front
The cosine similarity measure is used for_______
Back
all of above
7.
FLASHCARD QUESTION
Front
Data set {brown, black, blue, green, red} is an example of
Back
Nominal
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