Data Mining Assignment: Association Rules and Clustering Analysis

This data mining assignment solution analyzes association rules generated by XLMiner, exploring concepts like confidence levels and rule redundancy. It examines the impact of changing confidence percentages on the number of rules generated. The solution then delves into clustering techniques, including dendrogram analysis and the importance of data normalization. It presents outputs from hierarchical and K-means clustering, classifying customer segments based on flight transactions and bonus miles. The assignment compares the results of both clustering methods to determine the consistency of the findings, and discusses the implications of the analysis for business applications. The document also references several academic sources to support the analysis and findings.