Description
1. On describing discovered knowledge using association
rules
One of the major techniques in data mining involves the discovery of association rules.
These rules correlate the presence of a set of items with another range of values for another
set of variables. The database in this context is regarded as a collection of transactions,
each involving a set of items, as shown below.
Trans ID Items Purchased
101 milk, bread, eggs
102 milk, juice
103 juice, butter
104 milk, bread, eggs
105 coffee, eggs
106 coffee
107 coffee, juice
108 milk, bread, cookies, eggs
109 cookies, butter
110 milk, bread
1.1
Apply the Apriori algorithm on this dataset.
Note that, the set of items is {milk, bread, cookies, eggs, butter, coffee, juice}. You may use
0.2 for the minimum support value.
1.2
Show two rules that have a confidence of 0.7 or greater for an itemset containing three
items.
1
2. On describing discovered knowledge using classification
Classification is the process of learning a model that describes different classes of data and
the classes should be pre-determined. Consider the following set of data records:
RID Age City Gender Education Repeat Customer
101 20..30 NY F College YES
102 20..30 SF M Graduate YES
103 31..40 NY F College YES
104 51..60 NY F College NO
105 31..40 LA M High school NO
106 41..50 NY F College YES
107 41..50 NY F Graduate YES
108 20..30 LA M College YES
109 20..30 NY F High school NO
110 20..30 NY F college YES
2.1
Assuming that the class attribute is Repeat Customer, apply a classification algorithm to
this dataset.
3. On describing discovered knowledge using clustering
Consider the following set of two-dimensional records:
RID Dimension 1 Dimension 2
1 8 4
2 5 4
3 2 4
4 2 6
5 2 8
6 8 6
3.1
Use the K-means algorithm to cluster this dataset. You can use a value of 3 for K and can
assume that the records with RIDs 1, 3, and 5 are used for the initial cluster centroids
(means).
3.2
What is the difference between describing discovered knowledge using clustering and
describing it using classification.
2
