Association Rule Mining with Apriori Algorithm.pptx

Association Rule Mining with Apriori Algorithm.pptx

• 1.
  Mohammad Munaf Shaikh MCS.23.17
• 2.
  • Definition ofassociation rule mining: Association rule mining is a data mining technique that identifies patterns or relationships between items in large datasets to uncover frequent co-occurrences or associations between them. • Importance in data mining and market basket analysis: Association rule mining is vital in data mining for revealing patterns and relationships in datasets, particularly significant in market basket analysis for understanding customer purchasing behavior and optimizing business strategies. • Brief overview of Apriori Algorithm: The Apriori algorithm is a method for finding frequent item sets in transactional datasets by iteratively generating and pruning candidate item sets based on a minimum support threshold, enabling the discovery of association rules among items with high support and confidence.
• 3.
  • Explanation ofassociation rules: Association rules are relationships or patterns discovered in datasets that indicate the co-occurrence or association between items, revealing insights such as "if item A is purchased, then item B is also likely to be purchased," often expressed with metrics like support and confidence. • Format: {A} -> {B}, where A and B are item sets: • Support and confidence measures: Support measures the frequency of occurrence of an itemset, while confidence measures the reliability of an association rule between two items.
• 4.
  • Introduction tothe Apriori Algorithm: The Apriori algorithm is a classic method for mining frequent item sets in transactional datasets, iteratively generating and pruning candidate item sets to discover association rules with high support and confidence. • Designed for mining frequent item sets and generating association rules: Exactly, frequent item sets refer to subsets of items that occur together frequently in a dataset, surpassing a specified minimum support threshold. The Apriori principle states that if an item set is frequent, then all of its subsets must also be frequent, which is leveraged in the Apriori algorithm to efficiently prune candidate item sets during association rule mining.
• 5.
  • Explanation ofthe Apriori principle: The Apriori principle states that if an item set is frequent, then all of its subsets must also be frequent, guiding the Apriori algorithm to efficiently mine frequent item sets in transactional datasets. • Frequent item sets: subsets of items that appear frequently in the dataset: Frequent item sets are subsets of items that occur together frequently in a dataset, surpassing a specified minimum support threshold. • Pruning strategy to reduce the search space: Pruning strategies are techniques used to reduce the search space in algorithms like Apriori, where candidate itemsets that are unlikely to meet the desired criteria (e.g., minimum support) are discarded to improve efficiency.
• 7.
  • Visual exampleof association rule mining using the Apriori Algorithm: Using the Apriori algorithm, association rule mining identifies that customers who purchase diapers also tend to buy baby formula, demonstrating a strong association between these items in transactional data. • Sample transaction dataset with items purchased by customers: A sample transaction dataset includes records of customer purchases, where each transaction lists the items bought by a customer, such as "milk, bread, eggs." • Illustration of frequent item sets and generated association rules: Frequent item sets in a transaction dataset, like {milk, bread} and {bread, eggs}, generate association rules such as "if a customer buys bread, they are likely to also buy milk," revealing patterns in customer purchasing behavior.
• 8.
  • Real-world applicationswhere association rule mining is applied: • Market basket analysis • Recommender systems • Cross-selling and upselling strategies • Web usage mining
• 9.
  • - AssociationRule Mining with the Apriori Algorithm offers a powerful method to unveil patterns within datasets. • - By identifying frequent item sets and generating association rules, we gain insights into relationships between items. • - Applications span diverse fields including market basket analysis, recommendation systems, and more. • - Emphasize the algorithm's significance in data mining and encourage further exploration into its capabilities.