Task: Market Basket Analysis, Frequent Pattern-growth algorithm (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

Are you ready to uncover the secrets hidden in your transactional data? Building on your knowledge of market basket analysis, this course dives into the Frequent Pattern (FP-)Growth algorithm—a breakthrough in frequent pattern mining. Unlike the Apriori algorithm, FP-Growth uses a compact and efficient tree-based approach, allowing you to analyze large datasets with ease.

In this lesson, you will apply the FP-Growth algorithm to generate association rules from a real life industrial dataset.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.

Learning outcomes

By the end of this lesson, learners will be able to explain the FP-Growth algorithm, its key concepts, and how it differs from other frequent pattern-mining approaches like the Apriori algorithm.
By the end of this lesson, learners will be able to analyze and interpret the output of the FP-Growth algorithm, identifying meaningful patterns and insights in transactional data.
By the end of this lesson, learners will be able to critically analyze the advantages and limitations of FP-Growth, determining its suitability for specific datasets and dynamic operational contexts.