DATA MINING CONCEPTS AND
TECHNIQUES 4TH ED COMPREHENSIVE
EXAM 2026 SOLVED QUESTIONS AND
SOLUTIONS VERIFIED A+
◉ Why is feature selection important?
Answer: It helps in identifying and removing irrelevant features,
improving the effectiveness of data mining algorithms.
◉ What is data transformation?
Answer: Transforming attributes to new attributes, such as
converting numerical age into categories like young, middle-aged,
and elderly.
◉ What is the role of recent machine learning advancements in
feature discovery?
Answer: They focus on automatically discovering good features from
data without human intervention.
◉ What is the relationship between features and data points?
Answer: Features abstract data points, allowing for the learning of
rules for predictions.
, ◉ What is the impact of irrelevant features on data mining
algorithms?
Answer: They can hinder the performance of algorithms, making
feature selection and pruning necessary.
◉ What is the outcome of effective feature selection?
Answer: Improved accuracy and efficiency of data mining processes.
◉ What are the two general classes of data?
Answer: Nondependency-oriented data and dependency-oriented
data.
◉ What is nondependency-oriented data?
Answer: Data where objects do not have dependencies.
◉ What is dependency-oriented data?
Answer: Data with implicit or explicit dependencies between
objects.
◉ What is an example of nondependency-oriented data?
Answer: A multidimensional data set containing records with
features.
TECHNIQUES 4TH ED COMPREHENSIVE
EXAM 2026 SOLVED QUESTIONS AND
SOLUTIONS VERIFIED A+
◉ Why is feature selection important?
Answer: It helps in identifying and removing irrelevant features,
improving the effectiveness of data mining algorithms.
◉ What is data transformation?
Answer: Transforming attributes to new attributes, such as
converting numerical age into categories like young, middle-aged,
and elderly.
◉ What is the role of recent machine learning advancements in
feature discovery?
Answer: They focus on automatically discovering good features from
data without human intervention.
◉ What is the relationship between features and data points?
Answer: Features abstract data points, allowing for the learning of
rules for predictions.
, ◉ What is the impact of irrelevant features on data mining
algorithms?
Answer: They can hinder the performance of algorithms, making
feature selection and pruning necessary.
◉ What is the outcome of effective feature selection?
Answer: Improved accuracy and efficiency of data mining processes.
◉ What are the two general classes of data?
Answer: Nondependency-oriented data and dependency-oriented
data.
◉ What is nondependency-oriented data?
Answer: Data where objects do not have dependencies.
◉ What is dependency-oriented data?
Answer: Data with implicit or explicit dependencies between
objects.
◉ What is an example of nondependency-oriented data?
Answer: A multidimensional data set containing records with
features.
