DTSA 5505 - Data Mining Methods (Study
Cards) Actual Exam Complete Questions and
Correct Answers| 2026/27 Updated
market basket analysis
an unsupervised data mining technique for determining sales patterns
Frequent Pattern Mining
closed pattern X = no super-pattern Y given X with the same support; max-pattern
X = no super-pattern Y given X
Apriori Algorithm
A fast method of finding frequent itemsets, which also involves pruning non-
frequent items and self-joining of k-itemsets only if their first (k-1) items are the
same.
What challenges are there with apriori algorithm?
multiple scans of whole dataset, huge number of candidates, support counting of
all candidates
Improvements for apriori algorithm?
Partitioning, Sampling, Transaction reduction
Vertical data format
,Mining frequent itemsets using the ________________________ is a method
that transposes the rows of a given data set into columns.
FP-Growth Algorithm
If 'd' is frequent in DB | abc, then abcd is frequent (avoid candidate generation)
Association Rules
Association rules specify a relation between attributes that appears more
frequently than expected if the attributes were independent.
Correlation rules
Measure of dependent/correlated events: lift(A,B) = P(A U B) / P(A)P(B)
Rules of lift (correlation)
lift = 1 (independent), lift > 1 (positive), lift < 1 (negative)
Metarule-Guided Mining
P1 and P2 and ..... and Px => Q1 and Q2 and .... and Qy
Supervised Learning
Predefined classes, training data with groundtruth label
Unsupervised Learning
, No predefined classes; aims to identify potential clusters/patterns
Classification
categorical class labels (e.g. fraud detection)
Prediction
Continuous numerical values (e.g. stock prices)
Steps of Classification
1. Learning (Training Data, class labels, model construction)
2. Classification (test data, model evaluation)
What is considered in evaluation criteria?
accuracy, speed, interpretability, robustness, scalability
Decision Tree Induction
Basic algorithm: Attribute selection, attribute split
Key properties: top-down, recursive (divide-and-conquer, greedy)
Information Gain
Cards) Actual Exam Complete Questions and
Correct Answers| 2026/27 Updated
market basket analysis
an unsupervised data mining technique for determining sales patterns
Frequent Pattern Mining
closed pattern X = no super-pattern Y given X with the same support; max-pattern
X = no super-pattern Y given X
Apriori Algorithm
A fast method of finding frequent itemsets, which also involves pruning non-
frequent items and self-joining of k-itemsets only if their first (k-1) items are the
same.
What challenges are there with apriori algorithm?
multiple scans of whole dataset, huge number of candidates, support counting of
all candidates
Improvements for apriori algorithm?
Partitioning, Sampling, Transaction reduction
Vertical data format
,Mining frequent itemsets using the ________________________ is a method
that transposes the rows of a given data set into columns.
FP-Growth Algorithm
If 'd' is frequent in DB | abc, then abcd is frequent (avoid candidate generation)
Association Rules
Association rules specify a relation between attributes that appears more
frequently than expected if the attributes were independent.
Correlation rules
Measure of dependent/correlated events: lift(A,B) = P(A U B) / P(A)P(B)
Rules of lift (correlation)
lift = 1 (independent), lift > 1 (positive), lift < 1 (negative)
Metarule-Guided Mining
P1 and P2 and ..... and Px => Q1 and Q2 and .... and Qy
Supervised Learning
Predefined classes, training data with groundtruth label
Unsupervised Learning
, No predefined classes; aims to identify potential clusters/patterns
Classification
categorical class labels (e.g. fraud detection)
Prediction
Continuous numerical values (e.g. stock prices)
Steps of Classification
1. Learning (Training Data, class labels, model construction)
2. Classification (test data, model evaluation)
What is considered in evaluation criteria?
accuracy, speed, interpretability, robustness, scalability
Decision Tree Induction
Basic algorithm: Attribute selection, attribute split
Key properties: top-down, recursive (divide-and-conquer, greedy)
Information Gain
