Data Mining and Visualisation for Business Intelligence

Task Business Case Analysis 2 1. Association Rules (10%) This item requires the dataset [login to view URL] which can be found on the subject Interact site. Using XLMiner, apply association rules to the file Cosmetics-small.xls. Note: Do NOT include the Transaction # column in the XLMiner Data Range and accept the default Minimum Confidence (%) of 50. i. Interpret the first three rules in the output (in your own words). ii. Reviewing the first couple of dozen rules, comment on the rules’ redundancy and how you would assess the rules’ utility. iii. What would be the impact to the resulting rules if the Minimum Confidence (%) was raised to 75? Discuss why this occurs. 2. Cluster Analysis (10%) This item requires the dataset [login to view URL] which can be found on the subject Interact site. The dataset [login to view URL] contains information on 3999 passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers. a) Apply hierarchical clustering with Euclidean distance and Ward's method. Make sure to normalize the data first. How many clusters appear? b) What would happen if the data were not normalized? c) Compare the cluster centroid to characterize the different clusters, and try to give each cluster a label. d) Use K-means clustering with the number of clusters that you found above. Does the same picture emerge? e) Which clusters would you target for offers, and what types of offers would you target to customers in that cluster? Rationale This task assesses your progress towards meeting Learning Outcomes 3, 4 and 5. 3. Be able to compare and evaluate output patterns 4. Be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements 5. Be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set It also partly addresses Learning Outcomes 1 and 2.