Data Mining and Visualization: Association Rules, Clustering Analysis

This assignment solution addresses data mining and visualization techniques, focusing on association rules and clustering methods. The solution analyzes association rules with varying confidence levels, discusses rule redundancy, and highlights the significance of lift ratio and confidence levels in interpreting the rules. It then delves into hierarchical clustering, presenting a dendrogram and the characteristics of three clusters formed. The solution also explores K-means clustering, comparing its output with hierarchical clustering and classifying the clusters based on flight and non-flight transactions. The document includes references to relevant literature, providing a comprehensive overview of the data mining concepts and techniques used.