BIT 3201A BBIT 300 BAC 3201 – DATA MINING AND WAREHOUSING.

UNIVERSITY EXAMINATIONS: 2021/2022
EXAMINATION FOR THE DEGREE OF BSCIT, BSD, BBIT, BISF & BAC
BIT 3201A/ BBIT 300/ BAC 3201 – DATA MINING AND WAREHOUSING/
DATA MINING AND MANAGEMENT
FULL TIME/ PART TIME/DISTANCE LEARNING
DATE: DECEMBER, 2021 TIME: 3 HOURS
INSTRUCTIONS: Attempt Question 1 and Any Other Two Questions.

QUESTION 1: 20 Marks (COMPULSORY)
a) Describe the use of the following terms in the context of data mining highlighting why each is
necessary 4 Marks
i. Data Pruning
ii. Data Mining
iii. Prediction
iv. Outliers
b) Describe any four types of data that are gathered and be mined and state the type of
organizations that gather these types of data . 4 Marks
c) Discuss any four applications of data mining. 4 Marks
d) Using appropriate example discuss four types of noise and their sources 4 Marks
e) Discuss four ways in which the data that has been mined can be visually presented.
4 Marks
QUESTION 2: 15 Marks
a) With the help of a diagram, illustrate the Knowledge Discovery Process. 4 Marks
b) Discuss two benefits and two challenges of data mining 4 Marks
c) Using an appropriate example describe the concepts of support and confidence.
3 Marks
d) Describe and differentiate the discoveries from a data mining exercise. 4 Marks
i. Classification
ii. Clustering
QUESTION 3: 15 Marks
a) Discuss three factors that lead to the growth and popularity of data mining. 3 Marks
b) Describe three criteria upon which data mining system can be classified. 3 Marks
c) Discuss four factors that influence the selection and acquisition of data mining software.
4 Marks
c) Describe an OLAP highlighting four characteristics of OLAP 5 Marks
QUESTION 5: 20 Marks
a) In building a decision tree, three possible attributes are considered as split attributes, the
information gain for the attributes A, B, and C are 0.97, 0.029, and 0.15 respectively. Which
attribute should be selected for the split and why? 3 Marks
b) Consider a shop with the set of transactions shown in the table below. Assume that we wish
to find the association rules with at least 30% support and 60% confidence. Using the Apriori
algorithm, find the frequent item-sets and then the association rule(s) 12 Marks

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