Association Rules Analysis

Intermediate Level
22 hours to complete 3 weeks at 7 hours a week
Flexible Schedule

Di Wu

What You’ll Learn

Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection

Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.

Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Skills You’ll Gain

Data Mining Machine Learning Algorithms Data Processing Machine Learning Methods Algorithms Anomaly Detection Unsupervised Learning Data analysis Exploratory Data Analysis Feature Engineering Applied Machine Learning

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There are 5 modules in this course

This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.

This week we will briefly discuss association rule mining, such as closed and maxed patterns.

This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.

Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.

The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.