Data Mining Chapter 2
Data Mining Quiz: Test Your Knowledge
Welcome to the Data Mining Quiz! This engaging quiz consists of 28 questions designed to challenge your understanding of essential concepts in data mining. Whether you're a student, teacher, or data enthusiast, this quiz offers a great opportunity to assess your knowledge and learn more about the subject.
Key features of the quiz include:
- Multiple choice questions
- Instant feedback on your answers
- Variety of topics including statistics, data visualization, and distance measures
€¦â€¦. Is a data field, representing a characteristic or feature of a data object.
Attribute
Dimension
Fact
A and B
All of the following are attribute types except:
Descriptive
Nominal
Numeric
Binary
Given two objects represented by tuples (3, 5) and (2, 0). What is the Euclidean distance between two objects?
5.1
5
3
None
Given two objects represented by tuples (3, 5) and (2, 0). What is the Manhattan distance between two objects?
5.1
5
6
None
Euclidean distance is also known as ______________
L-1 norm
L-2 norm
L-3 norm
None
€¦.. Attribute has only a finite or countably infinite set of values while …. Attribute has real numbers as attribute values.
Discrete, continuous
Continuous, discrete
Qualitative, quantitative
Quantitative, qualitative
Value that occurs most frequently in the data?
Mean
Mode
Median
None
Middle value if odd number of values, or average of the middle two values otherwise.
Mean
Median
Mode
None
At symmetric data distribution
Mean = Mode
Mode = Median
None
A and B
The following graph represents …… data.
Positively correlated data
Uncorrelated data
Negatively correlated data
All of the above
All of the following measure data dispersion except:
Variance
Inter-Quartile range
Standard deviation
Mode
Basic statistical descriptions can be displayed graphically by:
Boxplot
Histogram
Scatter plot
All of the above
€¦â€¦ Graph display of tabulated frequencies, shown as bars
Boxplot
Histogram
Scatter plot
All of the above
€¦â€¦ is a numerical measure of how alike two data objects are.
Similarity
Proximity
Dissimilarity
All of the above
Is a numerical measure of how different two data objects are.
Similarity
Proximity
Dissimilarity
All of the above
We use data visualization to ….
To Gain insight into an information space
Search for patterns
Provide qualitative overview of large data sets
All of the above
In Pixel-Oriented Visualization Techniques for a data set of M dimensions we create ….. Windows on the screen.
M
M+1
2^M
M–1
Data set has more than mode
Unimodal
Bimodal
Trimodal
All of the above
Data objects are described by attributes.
True
False
Data objects are made up of data sets.
True
False
Dissimilarity value is lower when objects are more alike
True
False
Manhattan distance is represented by a direct line.
True
False
Similarity value is lower when objects are more alike.
True
False
We can achieve the Pixel-Oriented Visualization Techniques using direct visualization.
True
False
Scatterplot is one of the ways to achieve Geometric Projection Visualization Techniques.
True
False
In Pixel-Oriented Visualization Techniques the colors of the pixels reflect the corresponding dimensions.
True
False
We use data visualization because it helps find interesting regions and suitable parameters for further quantitative analysis
True
False
In Parallel Coordinates the axes are equidistant.
True
False
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