4 Association rules

“The power of association is stronger than the power of beauty; therefore the power of association is the power of beauty– John Ruskin”

Congratulations on finishing the first part of the book! You explored the basics of unsupervised learning and algorithms like k-means clustering, hierarchical clustering, DBSCAN, principal component analysis and others. It is expected that you have covered the mathematical concepts in the first part and created the Python codes to solve the exercise given at the end of each chapters.

Welcome to the second part of the book wherein we leverage the concepts learned in the first part and explore slightly more complex topics. We are starting with association rules in the chapter 4 of this book. All the very best!

4.1 Technical toolkit

4.2 Association rule learning

4.3 Building blocks of association rule

4.3.1 Support, confidence, lift, and conviction

4.4 Apriori algorithm

4.4.1 Python implementation

4.4.2 Challenges with Apriori algorithm

4.5 Equivalence class clustering and bottom-up lattice traversal (ECLAT)

4.5.1 Python implementation

4.6 Frequent-Pattern growth algorithm (F-P algorithm)

4.7 Sequence rule mining 

4.7.1 SPADE

4.8 Case study for association rules 

4.9 Limitations and Summary