DTSA 5504 - Data Mining Pipeline Comprehensive Resource
To Help You Ace 2026-2027 Exams Includes Frequently
Tested Questions With ELABORATED 100% Correct
COMPLETE SOLUTIONS
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1. What is data mining? - Correct Answer: The process of sorting through large data
sets to identify patterns and establish relationships to solve problems through data
analysis
2. What are the steps involved in data mining when viewed as a process of knowledge
discovery? - Correct Answer: Data Cleaning
Data Integration
Data Selection
Data Transformation
Data Mining
Pattern Evaluation
Knowledge Presentation
3. What are the data mining functionalities - Correct Answer: Characterization and
discrimination
Mining of frequent patterns, associations, and correlations Classification and regression
Clustering analysis
Outlier analysis
4. Data Characterization - Correct Answer: A summary of the general characteristics or
features of a target class of data. The data corresponding to the user-specified class is
typically collected by a query. For example, to study the characteristics of software
, products with sales that increased by 10% in the previous year, the data related to such
products can be collected by executing an SQL query on the sales database.
5. Data discrimination - Correct Answer: comparison of the target class with one or a
set of comparative classes
6. Data mining methodology challenges - Correct Answer: Mining various and new
kinds of knowledge
Mining knowledge in multidimensional space
Integrating new methods from multiple disciplines
Boosting the power of discovery in a networked environment
Handling uncertainty, noise, or incompleteness of data
Pattern evaluation and pattern- or constraint-guided mining
7. Explain one challenge of mining a huge amount of data in comparison with mining a
small amount of data. - Correct Answer: Algorithms that deal with data need to scale
nicely so that even vast amounts of data can be handled efficiently, and take short
amounts of time
8. What is an outlier? - Correct Answer: An object which does not fit in with the
general behavior of the model.
9. Does an outlier need to be discarded always? - Correct Answer: In most cases of data
mining, outliers are discarded. However, there are special circumstances, such as fraud
detection, where outliers can be useful.
10. The mode is the only measure of central tendency that can be used for nominal
attributes. (T/F) - Correct Answer: True. An example of this would be hair color, with
different categories such as black, brown, blond, and red. Which one is the most
common one?
,11. Nominal attribute - Correct Answer: refer to symbols or names of things. Categorical.
It can also be represented using a number, however, they are not meant to be used
quantitatively. Has no median, but has a mode
12. Binary Attributes - Correct Answer: A nominal attribute with only two categories or
states: 0 or 1, where 0 typically means that the attribute is absent, and 1 means that it is
present.
13. Ordinal Attributes - Correct Answer: An attribute with possible values that have a
meaningful order or ranking among them, but the magnitude between successive values
is not known.
14. Numeric Attributes - Correct Answer: Quantitative; that is, it is a measurable
quantity, represented in integer or real values. Can be interval-scaled or ratio-scaled.
15. Discrete Attribute - Correct Answer: has a finite or countably infinite set of variables
16. Continuous Attributes - Correct Answer: typically represented as floating-point
variables.
17. The mean is in general affected by outliers (T/F) - Correct Answer: True
18. Not all numerical data sets have a median. (T/F) - Correct Answer: False
19. What are the differences between the measures of central tendency and the measures
of dispersion? - Correct Answer: The measures of central tendency are the mean,
median, mode and midrange. They are used to measure the location of the middle or
the center of the data distribution, basically where the most values fall. Whereas, the
dispersion measures are the range, quartiles, interquartile range, the five-number
, summary, boxplots, the variance and standard deviation of the data. They are mainly
used to find an idea of the dispersion of the data, how is the data spread out, and to
identify outliers.
20. How would you catalog a boxplot, as a measure of dispersion or as a data visualization
aid? Why? - Correct Answer: As a data visualization aid. The boxplot shows how the
boundaries relate to each other visually, where the minimum, maximum values lie, and
the Interquartile ranges with a line signifying the median. It does not give you a specific
measure, but allows you to somewhat visualize the data set. For example, if you have a
boxplot for the grades in a class, if the box is closer to the minimum boundary then you
can see that most scores were low.
21. What do we understand by similarity measure? - Correct Answer: It quantifies the
similarity between two objects. Usually, large values are for similar objects and zero or
negative values are for dissimilar objects.
22. What is the importance of similarity measures - Correct Answer: They are important
because they help us see patterns in data. They also give us knowledge about our data.
They are used in clustering algorithms. Similar data points are put into the same clusters,
and dissimilar points are placed into different clusters.
23. What do we understand by dissimilarity measure and what is its importance? -
Correct Answer: Measuring the difference between to objects, the greater the difference
between two objects the higher the value.
24. What is the importance of dissimilarity measures - Correct Answer: The importance
of this is that in some instances, having two objects with low dissimilarity could mean
something negative. For example, cheating.
25. Discuss one of the distance measures that are commonly used for computing the
dissimilarity of objects described by numeric attributes. - Correct Answer: Euclidean
distance d(i, j) =sqrt((xi1 − xj1)^2 + (xi2 − xj2)^2 +··· )
To Help You Ace 2026-2027 Exams Includes Frequently
Tested Questions With ELABORATED 100% Correct
COMPLETE SOLUTIONS
Guaranteed Pass First Attempt!! Current Update!!
Instant Download Pdf
1. What is data mining? - Correct Answer: The process of sorting through large data
sets to identify patterns and establish relationships to solve problems through data
analysis
2. What are the steps involved in data mining when viewed as a process of knowledge
discovery? - Correct Answer: Data Cleaning
Data Integration
Data Selection
Data Transformation
Data Mining
Pattern Evaluation
Knowledge Presentation
3. What are the data mining functionalities - Correct Answer: Characterization and
discrimination
Mining of frequent patterns, associations, and correlations Classification and regression
Clustering analysis
Outlier analysis
4. Data Characterization - Correct Answer: A summary of the general characteristics or
features of a target class of data. The data corresponding to the user-specified class is
typically collected by a query. For example, to study the characteristics of software
, products with sales that increased by 10% in the previous year, the data related to such
products can be collected by executing an SQL query on the sales database.
5. Data discrimination - Correct Answer: comparison of the target class with one or a
set of comparative classes
6. Data mining methodology challenges - Correct Answer: Mining various and new
kinds of knowledge
Mining knowledge in multidimensional space
Integrating new methods from multiple disciplines
Boosting the power of discovery in a networked environment
Handling uncertainty, noise, or incompleteness of data
Pattern evaluation and pattern- or constraint-guided mining
7. Explain one challenge of mining a huge amount of data in comparison with mining a
small amount of data. - Correct Answer: Algorithms that deal with data need to scale
nicely so that even vast amounts of data can be handled efficiently, and take short
amounts of time
8. What is an outlier? - Correct Answer: An object which does not fit in with the
general behavior of the model.
9. Does an outlier need to be discarded always? - Correct Answer: In most cases of data
mining, outliers are discarded. However, there are special circumstances, such as fraud
detection, where outliers can be useful.
10. The mode is the only measure of central tendency that can be used for nominal
attributes. (T/F) - Correct Answer: True. An example of this would be hair color, with
different categories such as black, brown, blond, and red. Which one is the most
common one?
,11. Nominal attribute - Correct Answer: refer to symbols or names of things. Categorical.
It can also be represented using a number, however, they are not meant to be used
quantitatively. Has no median, but has a mode
12. Binary Attributes - Correct Answer: A nominal attribute with only two categories or
states: 0 or 1, where 0 typically means that the attribute is absent, and 1 means that it is
present.
13. Ordinal Attributes - Correct Answer: An attribute with possible values that have a
meaningful order or ranking among them, but the magnitude between successive values
is not known.
14. Numeric Attributes - Correct Answer: Quantitative; that is, it is a measurable
quantity, represented in integer or real values. Can be interval-scaled or ratio-scaled.
15. Discrete Attribute - Correct Answer: has a finite or countably infinite set of variables
16. Continuous Attributes - Correct Answer: typically represented as floating-point
variables.
17. The mean is in general affected by outliers (T/F) - Correct Answer: True
18. Not all numerical data sets have a median. (T/F) - Correct Answer: False
19. What are the differences between the measures of central tendency and the measures
of dispersion? - Correct Answer: The measures of central tendency are the mean,
median, mode and midrange. They are used to measure the location of the middle or
the center of the data distribution, basically where the most values fall. Whereas, the
dispersion measures are the range, quartiles, interquartile range, the five-number
, summary, boxplots, the variance and standard deviation of the data. They are mainly
used to find an idea of the dispersion of the data, how is the data spread out, and to
identify outliers.
20. How would you catalog a boxplot, as a measure of dispersion or as a data visualization
aid? Why? - Correct Answer: As a data visualization aid. The boxplot shows how the
boundaries relate to each other visually, where the minimum, maximum values lie, and
the Interquartile ranges with a line signifying the median. It does not give you a specific
measure, but allows you to somewhat visualize the data set. For example, if you have a
boxplot for the grades in a class, if the box is closer to the minimum boundary then you
can see that most scores were low.
21. What do we understand by similarity measure? - Correct Answer: It quantifies the
similarity between two objects. Usually, large values are for similar objects and zero or
negative values are for dissimilar objects.
22. What is the importance of similarity measures - Correct Answer: They are important
because they help us see patterns in data. They also give us knowledge about our data.
They are used in clustering algorithms. Similar data points are put into the same clusters,
and dissimilar points are placed into different clusters.
23. What do we understand by dissimilarity measure and what is its importance? -
Correct Answer: Measuring the difference between to objects, the greater the difference
between two objects the higher the value.
24. What is the importance of dissimilarity measures - Correct Answer: The importance
of this is that in some instances, having two objects with low dissimilarity could mean
something negative. For example, cheating.
25. Discuss one of the distance measures that are commonly used for computing the
dissimilarity of objects described by numeric attributes. - Correct Answer: Euclidean
distance d(i, j) =sqrt((xi1 − xj1)^2 + (xi2 − xj2)^2 +··· )
