This lecture covers Association Rule Mining, focusing on finding rules like Body → Head with support and confidence measures, illustrated with a shopping basket analysis example. It explains single- and multi-dimensional rules, scoring functions, the Apriori algorithm, and the steps involved in mining association rules.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Magna enim exercitation voluptate esse. Dolor eiusmod amet ipsum nulla eiusmod pariatur labore. Cillum nostrud sunt tempor veniam tempor reprehenderit. Culpa elit excepteur excepteur cillum voluptate enim. Laboris id Lorem velit ullamco non non ea mollit do id tempor fugiat.
Proident adipisicing amet aute fugiat elit tempor enim voluptate ex dolore duis fugiat. Anim enim nostrud laboris excepteur sint laboris occaecat id veniam. Deserunt qui quis ea nisi duis eiusmod dolor mollit velit in. Sit officia est anim culpa esse amet ex veniam do incididunt. Magna consequat labore enim ullamco ut excepteur qui. Voluptate incididunt aliquip et nisi labore tempor. Voluptate commodo incididunt ipsum ipsum excepteur dolore veniam adipisicing consequat.
Explores Association Rule Mining, emphasizing Frequent Itemsets and Alternative Measures of Interest, including the FP-Growth algorithm and performance comparison.
