BADM 12.1 Association Rules Part 1

What are association rules?; Operationalizing rules; Association rules vs. collaborative filtering; Antecedent and consequent; Frequent itemsets and the concept of Support; The Apriori algorithm
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on RUclips.
For more information:
www.dataminingbook.com
/ gshmueli
/ dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Networks: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

[@aiapplicationswithshailey3600]

Nice business and application oriented approach rather than the usual ones which involve products xijs and sets