Association Data Mining In this Assignment you will explore unsupervised knowledge discovery…
Association Data Mining
In this Assignment you will explore unsupervised knowledge discovery techniques of association rule mining and sequential patterns mining. In particular you will apply some of the techniques discussed in the lectures by tracing the algorithms using sample data sets.
Consider the following transaction database. Each row represents a single transaction in which the specified items have been purchased.
|
Transaction ID |
Items Purchased |
|
1 |
A,B,C,D |
1. Applying the Apriori algorithm with minimum support of 30% find all the frequent item sets in the data set. For each step in the algorithm, give the list of frequent item sets that satisfy minimum support (i.e., for each iteration i, give the set Li along with the support values for the frequent item sets).
2. From the maximal frequent item sets in part a, generate all association rules that meet a minimum confidence of 75%. In addition to the confidence also specify the Lift (improvement) values for each of the final set of rules you discovered.
