Data Mining Assignment: Analysis of Association Rules and Clustering

This assignment solution delves into data mining techniques, focusing on association rules and clustering analysis. It examines two association rules, identifying a redundant rule based on support and confidence levels. The solution then explores hierarchical clustering, interpreting a dendrogram to determine cluster numbers and discusses the importance of data normalization. Furthermore, it presents a comparison between hierarchical and K-means clustering, highlighting differences in cluster labeling and the implications of these variations. The solution also provides insights into the impact of minimum confidence levels on association rule utility and discusses the potential offers. The assignment leverages XLMiner for analysis and draws upon relevant literature to support its findings.