Data Mining Assignment: XL Miner Association Rules & Clustering

This assignment solution focuses on data mining techniques, specifically association rules and clustering analysis using XL Miner. The solution analyzes association rules, identifying redundant rules based on confidence levels and support. It then explores hierarchical and K-means clustering, comparing their outputs and interpreting the characteristics of different customer segments, such as "Middle Class Flyers," "High Networth Flyers," and "Non-Frequent Flyers." The assignment highlights the importance of data normalization in clustering and discusses the differences in results obtained from various clustering methods. The solution also provides references to relevant literature on data mining and business analytics.