ITC516 Data Mining And Visualisation For Business Intelligence

"Cosmetics small xls" data file must be only used for the assignment task one which is association. so assignment is self explanatory, however i am trying to explain it in simple words. PART 1 ,task 1, once association is applied on cosmetics data set , than it will give dozens of rules, however you need to choose the first three rules and interpret.

 

Explain those rules in your own words. the real art is explaining those rules in words. so please do that properly. for this task please check slide no 35 of the the below attached lecture slide "mining relations"however do read the whole lecture slides as that will be helpful for taks no 1.

 

Second task is to interpret the 24 rules and talk about the redundant ones and how you interrupt the utilities for these depending upon what you interpret. PART 3 third task is about increasing the confidence from 50 to 75 percent and applying association. after association explain the first few rules and talk about the differences or changes. after 50% and 75 percent .

It is apparent from the inputs that minimum confidence is considered as 50%.

• Comment on the primary three rules as per the list of rules table resulted through XLMiner is shown below (Ana, 2014).

Rule 1

It is apparent from the first row of the list of rules table that there is 100% confidence that a person who is going to buy brush item will also buy nail polish item.   

Rule 2

It is apparent from the second row of the list of rules table that there is 63.22 % confidence that a person who is going to buy nail polish item will also buy brush item.  

Rule 3

It is apparent from the third row of the list of rules table that there is 59.20 % confidence percentage that a person who is going to buy nail polish item will also buy bronzer item.

In this context, it is essential to note that there are two key characteristics of any association rule namely the lift ratio and also confidence level. The importance or support of an underlying rule is characterized by the lift ratio which ideally should be higher as association rules are arranged in decreasing order. Also, the confidence level is indicative of the conditional probability of the consequent purchase happening assuming antecedent purchase has happened.

• In order to analyze the first 24 rules, the support level is decreased to 50 which leads to the following rules.

Redundancy of association rules is often a common problem and hence rectifications need to be made in the form of deleting such rules whose incremental value is lacking as they tend to communicate the same information as has been communicated through a separate rule. In case of given cosmetic data and the associated output indicated above, there are a number of redundant rules that are identified below (Ragsdale, 2014).

• Rule 2 (with respect to Rule 1 as same lift ratio and predictable output)
• Rule 4(with respect to Rule 3 as same lift ratio and predictable output)
• Rule 6(with respect to Rule 5 as same lift ratio and predictable output)
• Rule 8(with respect to Rule 7 as same lift ratio and predictable output)
• Rule 10(with respect to Rule 9 as same lift ratio and predictable output)
• Rule 12(with respect to Rule 11 as same lift ratio and predictable output)
• Rule 14(with respect to Rule 13 as same lift ratio and predictable output)
• Rule 16(with respect to Rule 15 as same lift ratio and predictable output)

It needs to be understood that association rules are derived so that key insights into the consumer buying behavior needs to be understood. Hence, any redundant rules need to be deleted. Further,  In the interpretation of the remaining rules, two critical parameters essentially relate to the lift ratio and the confidence level which essentially determine the significance and support to the underlying rule. In this manner, vital information may be communicated with respect to the expected buying behavior of the customer which then can be used for decision making. For instance, items such as brush and nail polish may be placed on closed proximity so as to facilitate customer buying. Also, specific consumer traits that are most profitable to a given company related to purchase of cosmetics can be encouraged (Shumulei et. al., 2016).

It is apparent from the above shown XLMiner result that when the minimum confidence as 0.75 is considered then there is only single rule appears and rest rules has been disappeared. This is because all the relevant rules which have confidence percentage inferior than 75% have disappeared. Hence as a thumb rule, it can be estimated that increasing the relevant confidence level would lead to a diminishing of the rules displayed and it is quite possible that no rule is displayed. Here also, if the rules highlighted were more than one, they would have been arranged in the decreasing order of their respective lift ratio. Also, it is important that the minimum confidence level is the prerogative of the researcher based on the underlying task at hand (Shumueli, et. al., 2016).

• In order to find the exact number of clusters derived from the given set of data, dendrogram would be taken into account.

The dendrogram has been prepared through XLMiner Analytical platform for cluster analysis.If one should make a clear horizontal line starting from the cutoff distance,, then it can be cited that only three clusters has been derived for the data. This is apparent from the fact that the horizontal line would tend to intersect at three different places. Therefore, three clusters have been resulted from the data (Ana, 2014).

• The set of issues that would result when the data is normalized before performing clustering analysis.
• A key component of the clustering process is the distance between centroid which tends to get distorted owing to usage of non-normalised data.
• As a result, the overall accuracy of the clustering process is compromised with the scale providing to be a significant factor.

The above figure clearly illustrated the distorted cutoff distance which would have adverse impact on the cluster formation as has been outlined. Hence, it is always prescribed that normalisation must be done during the clustering process as in the absence of the this process, the utility of the clustering process and the output derived may be adversely impacted.

Also, the relevant output related to clustering stages is indicated below.

• The three clusters derived from the Ward process now need to be labeled based on the attributes of the clusters. For these the individual parameters of the various clusters need to be studied so to understand the common traits. Based on those traits, a particular label may be extended to these clusters that are formed using the hierarchical clustering.

In the above case, cluster one tends to have low non-flight bonus transactions and also flying frequency is low. Further the balance of point seems low when seen in the context of the other two clusters. Hence, it would be appropriate to club these under the “Middle Class Flyers” group (Ana, 2014).In the cluster 2 as highlighted above, the noticeable parameters are high flight transactions coupled with high balance. Besides, the non-flight transactions are also higher. Hence these may be labeled as “High Networth Flyers”.

In the cluster 3 as highlighted above, the noticeable parameters are low flight transactions but very high non-flight transactions. In fact the non-flight transactions for the group or cluster tend to exceed the other two. In view of these features, it would be appropriate to label these as “Non-Frequent Flyers”.

• In the given case, the data on airlines for understanding the behavior of the customers has to be run through two different clustering techniques. One of these has been already carried out in the form of hierarchical clustering. The other is the K-Means Clustering.  The output which can facilitate a comparison between the two is highlighted below (Ragsdale, 2014).

Even though both clustering techniques tend to provide only three clusters, but there is a difference in the cluster definition which can be made out by using an example. Consider any one cluster such as Cluster 1. Based on the parameters associated with the cluster, it would be appropriate to label this as “High Networth Flyers”. Thus, there is apparently difference in clustering pattern as this cluster 1 belonged to the middle class flyers.  Also, this understanding can be further extended to other clusters as well. If we consider cluster 2 attributes from the above K-Means Clustering output, it becomes apparent that these tend to have the lowest balance from the three clusters available. Also, the flying frequency tends to be less but simultaneously there non-flight bonus transactions are also quite less. Hence, it would be appropriate to label this cluster under the label of “Middle Class Flyer”. This is in sharp contrast with the respective result of hierarchical clustering where this particular cluster belonged to the “High Networth Flyers”. Thus, it becomes evident that the cluster output which has been derived under the two clustering methodologies tend to give results that are quite different (Abramowics, 2013).

• The cluster targeting and concerned offers are discussed below.

The cluster 2 has been chosen as the target considering these are customers who have been associated with the airlines since long which is apparent if we consider the underlying enrollment data. Also, these tend to frequently fly and amass huge balances of award point along with bonus miles. Clearly, continued value delivering to these customers is of utmost importance for the EastWest airlines so that their loyalty is strengthened.

Also, cluster 3 numerically represents a potent segment which cannot be ignored. The key feature is the high non-flight bonus transactions with low flight bonus transactions. As a result, intervention through offer is required so as to ensure that this behavior to some extent can be altered and prove beneficial for the airlines.

Reference

Abramowics, W. (2013) Business Information Systems Workshops: BIS 2013 International Workshops (5^th ed.). New York: Springer.

Ana, A.  (2014) Integration of Data Mining in Business Intelligence System (4^th ed.). Sydney: IGA Global

Ragsdale, C. (2014) Spread sheet Modelling and Decision Analysis: A Practical Introduction to Business Analytics (7^th ed.). London: Cengage Learning.

Shumueli, G., Bruce, C.P., Yahav, I., Patel, R. N., Kenneth, C., & Lichtendahl, J. (2016) Data Mining For Business Analytics: Concepts Techniques and Application (2^nd ed.).London: John Wiley & Sons.