Association Rules and Outlier Analysis

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 ca

• Understand unsupervised learning methods focused on association rules and outlier detection
• Comprehend applications of frequent patterns and association rules to discover item relationships
• Apply statistical and distance-based outlier detection methods to identify anomalies

• Basic knowledge of data analysis
• Fundamental concepts of machine learning