Data Mining and Warehousing

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

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

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

Front

Minkowski Distance is a generalization of Euclidean Distance and it is equivalent to Euclidean Distance when r is equal to

Back

2

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

Front

The cosine similarity measure is used for_______

Back

all of above

Front

Data set {brown, black, blue, green, red} is an example of

Back

Nominal

Front

Nominal and ordinal attributes can be collectively referred to as_________ attributes.

Back

Qualitative

Front

Which of the following is a data reduction technique? a. Clustering, b. Classification, c. Sampling, d. Regression

Back

Sampling

Front

Which of the following is not a measure of association used in association rule mining? a. Support, b. Confidence, c. Lift, d. Entropy

Back

Entropy