Data Mining: Frequent Patterns and Association Rules

Frequent Patterns Mining

• The purpose of frequent patterns mining is to identify patterns or relationships in data.

Association Rule Mining

• In association rule mining, X → Y represents a rule where X (antecedent) implies Y (consequent).
• A k-itemset is a set of k items that occur frequently together in a dataset.

Support Count

• The support count of X represents the frequency of occurrence of item X in the dataset.

Data Analysis

• Association rule mining is typically used to analyze transactional or categorical data.

Application of Association Rule Mining

• The main application of association rule mining is to identify relationships between items and make recommendations.

Rule Analysis

• The support for the rule: Coffee, Milk → Bread represents the frequency of transactions that contain both Coffee and Milk and also contain Bread.
• The confidence for the rule: Eggs → Bread represents the probability of Bread being purchased given that Eggs have been purchased.

Apriori Algorithm

• If the min_support is set to 40% and the min_confidence to 60%, the algorithm will generate strong rules that meet both thresholds.
• The support for item B represents the frequency of occurrence of item B in the dataset.
• Candidate pruning involves removing itemsets that are not frequent from the candidate set to reduce the number of rules to be evaluated.
• The Apriori Property states that all non-empty subsets of a frequent itemset must also be frequent.
• The confidence for the rule: A → B in the Apriori Example represents the probability of B being purchased given that A has been purchased.
• The term 'Apriori' refers to a classic algorithm for mining association rules.

Explore the concepts of frequent patterns mining and association rules in data mining. Learn about algorithms, clustering, classification, and the application of frequent patterns mining on different types of data such as transactional, sequential, and graph data.