Itc516 Data Mining And Visualisation Assessment Answers

Business Case Analysis

1. Association Rules

This item requires the dataset Cosmetics-small.xls 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.
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

This item requires the dataset East West Airlines Cluster.xls which can be found on the subject Interact site.

The dataset East West Airlines Cluster.xls 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?

Answers

1. The relevant output from XL Miner is indicated below.

i) The most pivotal three rules that the above output refers to are outlined below.

Rule 1- If the event of purchase of brushes happens, there would be the purchase of nail polish also. The associated confidence with this rule is 100% which implies that the underlying probability for the same is essentially 1.
Rule 2- If the event of purchase of nail polish happens, there would be the purchase of brushes also. The associated confidence with this rule is 63.22% which implies that the underlying probability for the same is essentially 0.6322.
Rule 3: If the event of purchase of nail polish happens, there would be the purchase of bronzer also. The associated confidence with this rule is 59.19% which implies that the underlying probability for the same is essentially 0.5919.

ii) It is pertinent to outline the underlying definition of the redundancy of a rule. A rule may be termed as redundant in relation to the other rule if the confidence level and support is atleast the same as the latter for each dataset. In relation to the given rules, a rule which can be termed as redundant is rule 16 when compared with rule 17. Also, rule 2 has a similar redundancy situation even though the underlying confidence level is different. For the first rule, the level amounts to 100% as compared to 63.22 for the second one.

The utility of the rules lies in the determination of conditional probabilities which may link at patterns that often need to be complemented with the various theories and literature review. Also, the various rules tend to provide complementary support to each other which can be used in derivation of meaningful conclusions.

iii) When the minimum confidence level is raised to 75%, then the number of rules witnesses a decrease. This is because only those rules would be highlighted where the underlying confidence level is atleast 75%. Considering the output of the given case, it becomes apparent that there would be only one rule highlighted if the confidence level is increased to 75% which is the Rule #1. No other rule has this high confidence and hence no display of other rules.

2. a) The relevant output for clusters is represented in the form of dendrogram which is represented below.

Based on the above, assuming a cutoff point at a distance of 1000, there are three clusters that are visible in the dendrogram.

b) If the data normalisation is not carried out, then the following observations/issues may come into prominence.

In case the weights corresponding to all the variables is not the same, then the measurement of distance would be wrong as certain variables would be given prominence over the others on the basis of their underlying magnitude.
The measure would be dominated by the largest scale and thus, the results obtained would be highly influenced by the effect of the scale.

c) By comparing the centroid of the three clusters derived, the following conclusion may be drawn.

Cluster 1 - This can be labelled as middle class travellers on account of the centroid distance characteristics. Noticeable amongst this is that the spending for this tends to lie between cluster 2 and cluster 3.
Cluster 2- This can be labelled as high networth flyers who are regulars. These tend to have been associated with the company since long which is also indicated from the time enrolled which is the highest for this cluster. Also, their balance seems to be highest amongst the three clusters. Besides, these tend to lead the other clusters in terms of flying frequency, point collected and balance remaining.
Cluster 3- This can be labelled as non-frequent fliers which is primarily indicated from their flying frequency particularly in the last twelve months. As a result, most of their characteristics tend to be lower than the other two clusters. This is primarily on account of lesser frequency of travelling.

d) The output of the K-Mean clustering obtained from XL Miner is obtained below.

The above is not comparable to the output which was obtained from hierarchical clustering. It is because there is no matching of the clusters which becomes evident. For instance, in case of hierarchical clustering the balance amount for the Cluster 2 which indicates frequent fliers tends to be highest. This is not the case above since it seems that the cluster 1 is indicative of the frequent fliers based on the various parameters. Similarly, cluster 2 in the above output seems to denote the infrequent flier which in the hierarchical clustering is indicated by cluster 3. Thus, in effect all the three clusters are different for hierarchical clustering and K-mean clustering.

e) Cluster 3- It is apparent that this cluster presents a huge amount of opportunity for the airlines considering that there travel frequency can be increased.

Offers: More bonus points can be extended if the number of travels exceeds a particular number, Also, better offers can be extended if frequent flyer card is availed by such customers.

Cluster 1: This is a booming cluster which can lead to future growth for the company considering the increase in income levels.

Offers: Increasing the reward points available on the frequent flier card usage. Also, extension of special offers coupled with higher bonus points on special occasions so as to increase the frequency of travel.

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