UNIVERSITY EXAMINATIONS: 2018/2019
EXAMINATION FOR THE DEGREE IN BACHELOR OF
SCIENCE IN INFORMATION TECHNOLOGY/BACHELOR
OF BUSINESS INFORMATION TECHNOLOGY
BIT3201 BBIT300 DATA WAREHOUSING AND DATA MINING
MODE: FULL TIME/PART TIME/DISTANCE LEARNING
ORDINARY EXAMINATIONS
DATE: MARCH.2019 DURATION: 2 HOURS
INSTRUCTIONS: Answer question ONE and any other TWO questions
QUESTION ONE [30 MARKS]
a) Define the following terms:
i. Data cube
ii. Dimension
iii. Dimension table
iv. Schema
4 Marks
a) Describe the generic steps in classification method in data mining
4 Marks
b) Clustering, in data mining, can be defined as a process of partitioning a set of data (or
objects) into a set of meaningful sub-classes, called clusters. In this regard, describe any four
requirements for clustering.
8 Marks
c) Discuss any FOUR issues used to measure the performance of a data mining system.
8 Marks
d) For each of the following tasks, state whether or not it is a data mining task.
i. Dividing the customers of a company according to their profitability.
ii. Monitoring the heart rate of a patient for abnormalities.
iii. Computing the total sales of a company.
iv. Sorting a student database based on student registration numbers.
v. Predicting the outcomes of tossing a fair coin
vi. Predicting the future stock price of a company using historical records.
6 Marks
QUESTION TWO [20 MARKS]
a) Explain the difference between Association rule mining and frequent item set mining
2 Marks
b) Differentiate between ordinal and nominal variable.
2 Marks
c) Describe any THREE data cleaning methods
3 Marks
d) Given two objects represented by the tuples (8,1,4,10) and (2,6,3,8) calculate the following
distances between the two objects. Calculate the minkowski distance if p=3.
4 Marks
e) Explain why it is advisable to separate a data warehouse from operational database.
3 Marks
f) Using a neat diagram discuss the various phases of knowledge discovery in databases.
6 Marks
QUESTION THREE [20 MARKS]
a) The following data was extracted from a certain retail shop in town.
i. Calculate the support for the rule {Bread, Butter}->{Sugar}
2 Marks
ii. Calculate the confidence for the rule in [i] above
2 Marks
iii. Calculate the lift the rule in [i] above
2 Marks
iv. Explain the use of confidence and support of a rule.
2 Marks
b) Explain the use of Apriori algorithm in data mining.
4 Marks
c) Discuss any FOUR dimensions of data quality
8 Marks
QUESTION FOUR [20 MARKS]
a) The main purpose of a data warehouse is to provide aggregate data like totals, average,
variance, trends etc. which is in a suitable format for decision making. From this point of
view:
i. Define the term data warehouse
2 Marks
ii. Discuss the FOUR key characteristics of a data warehouse
8 Marks
iii. Data warehousing can be defined as the process of constructing and using a data
warehouse. In this regard, describe the activities involved in designing and implementing
a data warehouse.
6 Marks
b) Data reduction is the process of minimizing the amount of data that needs to be stored in a
storage environment. Discuss any four techniques of data reduction.
4 Marks
QUESTIONS FIVE [20 MARKS]
Data mining can be defined as a process of uncovering of potentially useful information in the
data warehouse. From this stand point:
a) Discuss the challenges that face data mining with regard to:
i. Data mining methodology and user interaction issues
ii. Performance issues
6 Marks
b) Describe the various classifications of data mining techniques.
4 Marks
i. Marketing
ii. Crime management
4 Marks
c) Using a well labeled diagram, describe the architecture of a data mining system.
6 Marks
