Description
1. 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
2001 Meat, Potato, Onion
2002 Meat, Noodle
2003 Noodle, Spinach
2004 Meat, Potato, Onion
2005 Onion, Potato, Noodle
2006 Eggs, Spinach
2007 Eggs, Noodle
2008 Meat, Potato, Salt, Onion
2009 Salt, Spinach
2010 Meat, Potato
1.1 Apply the Apriori algorithm on this dataset.
Note that, the set of items is {Meat, Potato, Onion, Noodle, Spinach, Eggs, Salt}. You may use 0.3 for the minimum support value.
1.2 Show the rules that have a confidence of 0.8 or greater for an itemset containing three items.
2. 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:
ID
Age
City
Gender
Education
Profile
101
20-30
NY
F
College
Employed
102
31-40
NY
F
College
Employed
103
51-60
NY
F
College
Unemployed
104
20-30
LA
M
High School
Unemployed
105
41-50
NY
F
College
Employed
106
41-50
NY
F
Graduate
Employed
107
20-30
LA
M
College
Employed
108
20-30
NY
F
High School
Unemployed
109
20-30
NY
F
College
Employed
110 51-60 SF M College Unemployed
Assuming, that the class attribute is Profile, apply a classification algorithm to this dataset.
3. Clustering: Consider the following set of two-dimensional records:
RID Age Years of Service
101 30 5
102 50 25
103 50 15
104 25 5
105 30 10
106 55 25
3.1
Use the K-means algorithm to cluster this dataset. You can use a value of 2 for K and can assume that the records with RIDs 103, and 104 are used for the initial cluster centroids.
3.2
What is the difference between describing discovered knowledge using clustering and describing it using classification?
