Lecture 6 : Rule generation

Summary

In this lecture on association rule mining, the focus is on the Apriori algorithm, which identifies frequent item sets by reducing the number of candidate item sets using the Apriori principle. The process involves finding frequent item sets, generating rules, and testing their confidence. The principles of pruning and lattice structure help in efficient rule generation. Additionally, the concept of interestingness measures is introduced to evaluate the rules beyond simple support and confidence, including measures like lift, interest, and Jaccard coefficient. The lecture concludes by preparing for predictive models like classification in future lessons.

Takeaways

• 😀 The Apriori algorithm helps reduce the number of candidate item sets by using the Apriori principle, which prunes infrequent item sets.
• 😀 The process of finding association rules begins with identifying frequent item sets by checking their occurrence in transactions.
• 😀 The Apriori principle states that if an item set is not frequent, its supersets cannot be frequent either.
• 😀 Frequent item sets are used to generate association rules by splitting the item sets into left and right parts, creating various rule combinations.
• 😀 Confidence is the main criterion for evaluating the strength of generated rules, calculated by the ratio of the number of occurrences of the full item set to the left-side item set.
• 😀 Pruning rules based on the confidence threshold ensures that only the most relevant and significant rules are considered.
• 😀 Association rules can be evaluated using interestingness measures such as lift, interest, and the p-value to assess whether the rules are meaningful.
• 😀 Lift measures the strength of a rule by comparing the joint probability of items to their individual probabilities, indicating positive or negative correlation.
• 😀 Statistical independence is important in evaluating rules, helping determine whether events like buying tea and coffee are related or not.
• 😀 Unexpectedness is another evaluation measure that assesses how novel or surprising a rule is, based on existing domain knowledge.
• 😀 The lecture emphasizes the need to evaluate association rules not just based on confidence, but also using objective measures like lift, interest, and domain-specific criteria to ensure their validity.

Q & A

What is the main purpose of the Apriori algorithm in association rule mining?

The Apriori algorithm's main purpose is to reduce the number of candidate item sets by applying the Apriori principle. It identifies frequent item sets by pruning non-frequent ones, which helps in efficiently generating association rules.

How does the Apriori principle help in pruning candidate item sets?

The Apriori principle states that if an item set is not frequent, then any superset of that item set cannot be frequent either. This allows the algorithm to prune candidate item sets early, reducing the number of combinations to check.

What are the steps involved in generating frequent item sets using the Apriori algorithm?

The steps involve first finding frequent 1-item sets, then using those to generate frequent 2-item sets, and so on. Item sets are joined based on shared items, and their frequency is tested. If a combination is frequent, it is used to generate larger item sets.

Can you explain how two frequent item sets are joined to form a larger item set?

Two frequent item sets can be joined if they differ by only one item. For example, if 'a, b' and 'a, c' are both frequent, they can be joined to form 'a, b, c' as a candidate frequent item set.

What criteria are used to evaluate the association rules generated from frequent item sets?

Association rules are evaluated using two main criteria: support and confidence. Support measures the frequency of the item sets, while confidence measures the strength of the rule by calculating the probability of the consequent occurring given the antecedent.

How is the confidence of an association rule calculated?

Confidence is calculated as the ratio of the number of transactions that contain both the antecedent and consequent (e.g., A, B, C, D) to the number of transactions containing only the antecedent (e.g., A, B, C).

What is the lattice structure in association rule generation?

The lattice structure in rule generation organizes all possible item sets. Rules are generated by progressively moving one attribute from the left-hand side to the right-hand side of the rule, creating a hierarchical structure of rules.

How does the Apriori algorithm handle pruning when testing association rules?

Apriori algorithm prunes the rule lattice by eliminating rules that have a left-hand side with too many items, as these tend to have lower confidence. This helps in reducing the number of rules to test and improves efficiency.

What are interestingness measures, and why are they important in evaluating association rules?

Interestingness measures evaluate the validity and usefulness of association rules beyond just support and confidence. They include metrics like lift, interest, and the p-value, which help assess how relevant or surprising a rule is in a given domain.

Can you explain the concept of lift as an interestingness measure in association rule mining?

Lift is a measure of how much more likely two items are to occur together than would be expected if they were independent. A lift value greater than 1 indicates a positive correlation, while a value less than 1 indicates a negative correlation.