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Association Rules Analysis
  • Level Professional
  • Duration 23 hours
  • Course by University of Colorado Boulder
  • Offered by

About

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. Course Learning Objectives: By the end of this course, students will be able to: 1. Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection. 2. Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items. 3. Explore Apriori algorithms to mine frequent itemsets efficiently and generate association rules. 4. Implement and interpret support, confidence, and lift metrics in association rule mining. 5. Comprehend the concept of constraint-based association rule mining and its role in capturing specific association patterns. 6. Analyze the significance of outlier detection in data analysis and real-world applications. 7. Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points. 8. Understand contextual outliers and contextual outlier detection techniques for capturing outliers in specific contexts. 9. 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.

Modules

Introduction to Frequent Pattern Analysis

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Introduction to Frequent Pattern Analysis

2 Readings

  • Assessment Strategy
  • Activity Strategy

Frequent Itemsets and Association Rules

    • 1 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Frequent Itemsets and Association Rules Quiz

1 Videos

  • Frequent Itemsets and Association Rules

2 Readings

  • Frequent Itemsets Demo
  • Association Rules Demo

Association Rule Mining

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Association Rule Mining Quiz

1 Videos

  • Association Rule Mining

Apriori Algorithm

    • 1 Videos
    • 4 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Apriori Algorithm Quiz

1 Discussions

  • Apriori Algorithm Exploration Exercise

1 Videos

  • Apriori Algorithm

4 Readings

  • Apriori Algorithm Demo
  • FP Growth Algorithm Demo
  • Apriori Algorithm Case Study Online Retail
  • Apriori Algorithm Case Study

Constraint-Based Association Rule Mining

    • 1 Videos
Show info about module content

1 Videos

  • Constraint-based Association Rule Mining

Outliers

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Outliers Quiz

1 Discussions

  • Outliers Exploration Exercise

1 Videos

  • Outliers

2 Readings

  • Outliers Demo
  • Outliers Case Study - CC Fraud Detection

Case Study

    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Self Reflection

1 Discussions

  • Association Rule Exploration Exercise

1 Readings

  • Association Rule Case Study

Auto Summary

Discover the world of unsupervised learning with the "Association Rules and Outliers Analysis" course, designed to equip you with the essential skills for mastering association rules and outlier detection. This course delves deep into the realm of Big Data and Analytics, offering comprehensive insights into frequent patterns and the application of the Apriori algorithm. Led by industry experts at Coursera, this professional-level course spans a duration of 1380 minutes, ensuring an in-depth understanding and practical experience through interactive tutorials and real-world case studies. Key highlights include: - Grasping the core principles and significance of unsupervised learning methods. - Learning to mine and interpret frequent itemsets and generate association rules using Apriori algorithms. - Applying support, confidence, and lift metrics effectively. - Exploring constraint-based association rule mining for specific patterns. - Analyzing and applying various outlier detection methods, including statistical and distance-based approaches. - Understanding and detecting contextual outliers to capture anomalies in specific contexts. Ideal for professionals seeking to enhance their data analysis skills, the course offers flexible subscription options through Coursera, including Starter and Professional tiers. By the end of this course, you will be proficient in applying association rules and outlier detection techniques, enabling you to make informed decisions and uncover meaningful insights from diverse datasets. Join us to advance your expertise in unsupervised learning and elevate your analytics capabilities today!

Di Wu
Instructor

Di Wu