18 Questions
Which property distinguishes a ratio attribute from other types of attributes?
All four properties of distinctness, order, addition, and multiplication
What type of values do continuous attributes have?
Real numbers
Which measure represents the middle value of a dataset?
Median
What is the empirical formula relating mean, mode, and median?
$mean - mode = 3 \times (mean - median)$
Which type of attribute has distinctness and order properties but not addition?
Ordinal attribute
What distinguishes a discrete attribute from a continuous attribute?
Having a finite or countably infinite set of values
Which of the following statements about Euclidean distance is true?
It is a special case of the Minkowski distance when the parameter $r$ is set to 2.
In the context of dissimilarity measures, what is the significance of the upper limit?
It varies depending on the dissimilarity measure used.
Which of the following statements about the Minkowski distance is incorrect?
It is only applicable to data with numerical attributes.
Which of the following is a common property of distance measures like the Euclidean distance?
The distance between two data objects is always greater than or equal to zero.
In the context of dissimilarity measures, what does it mean when the dissimilarity between two data objects is low?
The two data objects are highly similar or alike.
Which of the following statements about the Minkowski distance is correct?
It reduces to the Manhattan distance when the parameter $r$ is set to 1.
In sampling with replacement, what is the key difference compared to sampling without replacement?
The same object can be picked up more than once
What is the key characteristic of stratified sampling?
Splitting the data into several partitions and then drawing random samples from each partition
What is the purpose of mapping data to a new space, such as through Fourier or wavelet transforms?
To transform the data into a new representation that may reveal patterns or structures not visible in the original space
What is the key difference between discretization using class labels and discretization without using class labels?
Discretization using class labels divides the data into equal frequency bins, while discretization without class labels divides the data into equal interval width bins
What is the purpose of attribute transformation, such as using simple functions like $x^k$, $\"\log(x)\"$, $e^x$, or $|x|$?
To map the values of an attribute to a new set of replacement values, where each old value is identified with one of the new values
If the income range is $12,000 to $98,000, and we want to normalize it to the range [0.0, 1.0], what is the normalized value of $73,600?
0.716
Test your knowledge on sampling methods such as sampling with replacement and stratified sampling, as well as data transformations like Fourier transform and wavelet transform. Explore concepts like sample size determination and data discretization.
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