The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying association rules and outlier detection techniques to diverse datasets.
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Course Learning Objectives:
By the end of this course, students will be able to:
- 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.
- Explore Apriori algorithms to mine frequent itemsets efficiently and generate association rules.
- Implement and interpret support, confidence, and lift metrics in association rule mining.
- Comprehend the concept of constraint-based association rule mining and its role in capturing specific association patterns.
- Analyze the significance of outlier detection in data analysis and real-world applications.
- Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.
- Understand contextual outliers and contextual outlier detection techniques for capturing outliers in specific contexts.
- Apply association rules and outlier detection techniques in real-world case studies to derive meaningful insights.
Throughout the course, students will actively engage in tutorials and case studies, strengthening their association rule mining and outlier detection skills and gaining practical experience in applying these techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in unsupervised learning tasks and make informed decisions using association rules and outlier detection techniques.
This course is part of the Data Analysis with Python Specialization.
Syllabus
Frequent Itemsets
Module 1
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.
Association Rule Mining
Module 2
This week we will briefly discuss association rule mining, such as closed and maxed patterns.
Apriori and FP Growth Algorithm
Module 3
This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.
Outliers
Module 4
Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.
Case Study
Module 5
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.
