Association Rule Learning and Apriori Algorithm

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Questions and Answers

What is the correct formula for calculating the confidence of an association rule A→B?

Confidence(A→B) = Support(A) / Support(B)

Confidence(A→B) = (Total no. of tuples) / (No. of tuples having both A and B)

Confidence(A→B) = (No. of tuples having A) / (Total no. of tuples)

Confidence(A→B) = (No. of tuples having both A and B) / (No. of tuples having A) (correct)

Which property does the Apriori algorithm utilize to prune the candidate itemsets?

Commutativity

Transitivity

Antimonotonicity (correct)

Monotonicity

If 'Tea, Biscuit' is a frequent itemset, which of the following is guaranteed to be a frequent itemset?

Tea (correct)

Coffee

Biscuit, Coffee

Tea, Coffee

What is the initial step in the Apriori algorithm for frequent itemset generation?

Generating one-item frequent patterns Signup and view all the answers

Given n items, how many possible candidate itemsets can be generated?

$2^n$ Signup and view all the answers

What is the maximum number of candidate association rules generated from a frequent itemset of size k?

$2^k - 2$ Signup and view all the answers

What happens if the minimum support threshold is set too high?

Miss interesting rare itemsets. Signup and view all the answers

Which rule would NOT be generated from the frequent itemset {A, B, C, D}?

A => C Signup and view all the answers

How is a candidate rule generated from existing rules?

By merging two rules that share the same prefix. Signup and view all the answers

What would be a consequence of setting the minimum support threshold too low?

Increased computational expense. Signup and view all the answers

Which of the following represents a pruned rule in the context of rule generation?

AD => BC Signup and view all the answers

Which of the following statements is correct regarding candidate rules?

Candidate rules can include low confidence associations. Signup and view all the answers

What is the impact of merging the rules CD => AB and BD => AC?

The resultant rule is D => ABC. Signup and view all the answers

What is the total number of association rules that can be generated from the frequent itemset {2,3,5}?

6 Signup and view all the answers

Which of the following association rules is considered strong based on a minimum confidence threshold of 70%?

{3,5} → 2 Signup and view all the answers

What bottleneck does the Apriori algorithm face in frequent-pattern mining?

Candidate generation and testing Signup and view all the answers

What is the purpose of using a hashing based technique in the Apriori algorithm?

To reduce candidate size Signup and view all the answers

What is the support count of the association rule {3,5} → 2?

2 Signup and view all the answers

What happens to a transaction that does not contain any frequent k-itemsets during future iterations?

It cannot contribute to frequent (k+1)-itemsets Signup and view all the answers

Given the frequent itemset {b, c, e}, what is the calculated confidence according to the example?

50% Signup and view all the answers

Which of the following is NOT a necessary condition for an association rule to be classified as strong?

Low Support count Signup and view all the answers

What is a key difference between FP-growth and the Apriori algorithm?

FP-growth is significantly faster than Apriori. Signup and view all the answers

Which of the following statements is true regarding the FP-tree path generation?

All combinations of sub-paths can be enumerated to find all frequent patterns. Signup and view all the answers

What is the role of the support count in the process of FP-growth?

It defines the minimum number of transactions required for an itemset to be considered frequent. Signup and view all the answers

What is a feature of the m-conditional FP-tree?

It only stores the conditional pattern bases for specific items. Signup and view all the answers

In the context of FP-growth, what is meant by 'minimum confidence'?

It is the percentage of transactions containing a certain pattern. Signup and view all the answers

Which operation is the fundamental one during the FP-tree building process?

Counting itemset occurrences Signup and view all the answers

How many times is the database scanned in the first step of the FP-growth algorithm?

Only once, to gather initial itemset counts. Signup and view all the answers

What is the impact of eliminating repeated database scans in FP-growth?

It reduces the time complexity of the process. Signup and view all the answers

What does multilevel association rule mining primarily focus on?

Finding relationships between items at different levels of granularity. Signup and view all the answers

Which of the following statements about reduced support is true?

It allows lower levels to have reduced minimum support. Signup and view all the answers

What is a primary characteristic of uniform support in multilevel association rule mining?

The same minimum support for all levels. Signup and view all the answers

Which search strategy involves filtering by k-itemsets in multilevel association rule mining?

Level-cross filtering by k-itemset. Signup and view all the answers

Which type of association rule includes more than one dimension or predicate?

Multi-dimensional Association Rules. Signup and view all the answers

What type of attributes are characterized by having a finite number and no implicit order?

Categorical attributes. Signup and view all the answers

In reduced support, what happens if the support threshold is set too high?

Only high-level associations are generated. Signup and view all the answers

Which of the following is an example of a hybrid-dimension association rule?

age(X, '19-25') ∧ buys(X, 'popcorn') → buys(X, 'coke') Signup and view all the answers

Study Notes

Rule Generation

Non-empty subsets of a frequent itemset are generated, where the subset and its complement must meet minimum confidence requirements.
For a frequent itemset like {A, B, C, D}, candidate rules include combinations such as ABC → D and AB → CD.
The total candidate association rules for an itemset of size k is calculated as 2^k - 2.

Apriori Algorithm for Rule Generation

Rules are created by merging two rules that share the same prefix in the consequent.
Example: Joining CD → AB and BD → AC produces D → ABC.
Prune candidate rules that do not meet confidence thresholds, based on their subsets.

Support Distribution

Setting minimum support (minsup) too high risks missing interesting rare itemsets; too low leads to computational inefficiency.
A single threshold may not be effective for varying itemset distributions.

Working of the Apriori Algorithm

Employs a min support threshold (e.g., 50%) to filter itemsets.
Generates frequent itemsets through successive scanning of the transaction database.
An example database shows itemset support counts derived from transaction IDs (TID).

Association Rule Fundamentals

Support measures how frequently items appear together relative to the total dataset.
Confidence indicates how often an association holds true in relation to the frequency of the antecedent.

Frequent Itemset Generation

Given n items, the total possible candidate itemsets is 2^n.
Frequent itemsets include combinations like {A, B} and {C, D}, depending on their support counts.

Steps in Apriori Algorithm

Generate frequent itemsets of increasing size, leveraging the antimonotonicity property, which states any subset of a frequent itemset must also be frequent.
Generate association rules from the final frequent itemsets.

Bottlenecks of Frequent-Pattern Mining

Apriori can be inefficient due to extensive database scans and candidate generation.
Techniques like hashing and transaction reduction can mitigate these bottlenecks.

FP-Growth Algorithm

FP-Growth is faster than Apriori, eliminating candidate generation and employing a more compact data structure.
Frequent Pattern Trees (FP-trees) are constructed, allowing for quicker pattern mining through a single database pass.

Multilevel Association Rule Mining

This technique looks for associations at different granularities, recognizing dimensional relationships.
Utilizes both uniform support (same threshold across levels) and reduced support (lower thresholds at lower levels).

Multi-dimensional Association Rules

Unlike single-dimensional rules, multi-dimensional rules involve multiple predicates or dimensions.
Examples include demographic attributes and purchasing behavior, indicating complex relationships between data points.

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Description

This quiz covers key concepts of association rule learning, focusing on the generation of rules from frequent itemsets using the Apriori algorithm. It emphasizes the importance of support and confidence levels in determining candidate rules and highlights the balance needed for minimum support thresholds. Test your understanding of these foundational data mining techniques.