DATA MINING CONCEPTS AND
TECHNIQUES 4TH ED EXAM SCRIPT 2026
COMPLETE QUESTIONS AND ANSWERS
ALREADY PASSED
◉ List and briefly define at least two classification techniques
Answer: Decision tree analysis. Decision tree analysis (a machine-
learning technique) is arguably the most popular classification
technique in the data mining arena.• Statistical analysis. Statistical
classification techniques include logistic regression and
discriminant analysis, both of which make the assumptions that the
relationships between the input and output variables are linear in
nature, the data is normally distributed, and the variables are not
correlated and are independent of each other.• Case-based
reasoning. This approach uses historical cases to recognize
commonalities in order to assign a new case into the most probable
category.• Bayesian classifiers. This approach uses probability
theory to build classification models based on the past occurrences
that are capable of placing a new instance into a most probable class
(or category).• Genetic algorithms. The use of the analogy of natural
evolution to build directed search-based mechanisms to classify data
samples.• Rough sets. This method takes into account the partial
membership of class labels to predefined categories in building
models (collection of rules) for classification problems.
, ◉ What are some of the criteria for comparing and selecting the best
classification technique
Answer: The amount and availability of historical data The types of
data, categorical, interval, ration, etc.
◉ Define Gini Index
Answer: Measures in the inequality of incomes within a country
◉ Briefly describe the general algorithm used in decision trees
Answer: A general algorithm for building a decision tree is as
follows: Create a root node and assign all of the training data to it.
Select the best splitting attribute. Add a branch to the root node for
each value of the split. Split the data into mutually exclusive (non-
overlapping) subsets along the lines of the specific split and mode to
the branches. Repeat steps 2 and 3 for each and every leaf node until
the stopping criteria is reached (e.g., the node is dominated by a
single class label).
◉ What is an ensemble model in data mining? What are the pros and
cons of ensemble models?
Answer:
◉ Give examples of situations in which cluster analysis would be an
appropriate data mining technique
TECHNIQUES 4TH ED EXAM SCRIPT 2026
COMPLETE QUESTIONS AND ANSWERS
ALREADY PASSED
◉ List and briefly define at least two classification techniques
Answer: Decision tree analysis. Decision tree analysis (a machine-
learning technique) is arguably the most popular classification
technique in the data mining arena.• Statistical analysis. Statistical
classification techniques include logistic regression and
discriminant analysis, both of which make the assumptions that the
relationships between the input and output variables are linear in
nature, the data is normally distributed, and the variables are not
correlated and are independent of each other.• Case-based
reasoning. This approach uses historical cases to recognize
commonalities in order to assign a new case into the most probable
category.• Bayesian classifiers. This approach uses probability
theory to build classification models based on the past occurrences
that are capable of placing a new instance into a most probable class
(or category).• Genetic algorithms. The use of the analogy of natural
evolution to build directed search-based mechanisms to classify data
samples.• Rough sets. This method takes into account the partial
membership of class labels to predefined categories in building
models (collection of rules) for classification problems.
, ◉ What are some of the criteria for comparing and selecting the best
classification technique
Answer: The amount and availability of historical data The types of
data, categorical, interval, ration, etc.
◉ Define Gini Index
Answer: Measures in the inequality of incomes within a country
◉ Briefly describe the general algorithm used in decision trees
Answer: A general algorithm for building a decision tree is as
follows: Create a root node and assign all of the training data to it.
Select the best splitting attribute. Add a branch to the root node for
each value of the split. Split the data into mutually exclusive (non-
overlapping) subsets along the lines of the specific split and mode to
the branches. Repeat steps 2 and 3 for each and every leaf node until
the stopping criteria is reached (e.g., the node is dominated by a
single class label).
◉ What is an ensemble model in data mining? What are the pros and
cons of ensemble models?
Answer:
◉ Give examples of situations in which cluster analysis would be an
appropriate data mining technique
