Knom 722- Knowledge Discovery Question Paper
Knom 722- Knowledge Discovery
Course:Master Of Knowledge Management
Institution: Kisii University question papers
Exam Year:2011
KISII UNIVERSITY COLLEGE
FIRST YEAR EXAMINATIONS FOR THE AWARD OF THE DEGREE OF MASTER OF KNOWLEDGE MANAGEMENT.
SECOND SEMESTER 2010/2011
(OCTOBER 2011)
KNOM 722- KNOWLEDGE DISCOVERY
STREAM: KNOM Y1 S2 TIME: 3 HOURS
DAY: MONDAY, 2.00 – 5.00 P.M DATE: 24/10/2011
INSTRUCTIONS
1. Do not write anything on this question paper.
2. Answer question ONE and any TWO from the remaining questions.
SECTION A
1. i. Explain the concept of knowledge discovery process. (2 marks)
ii. Explain any four application areas of data mining. (4 marks)
iii. Describe the three technologies that support data mining for business applications. (6 marks)
iv. Imagine you are the chief data analyst responsible for deploying a knowledge discovery project
related to mining data gathered by a major insurance company. The goal is to discover fraud patterns.
The customer's data are stored in a well-mantained data ware house, and a team of data analysts who are
familiar with the data are at your disposal. The management stresses the importance of data analysis,
documentation and deployment of the developed solutions. Which KDP module will you choose to carry out
the project and why? (8 marks)
SECTION B
2. a. Explain the following acronyms and terms: (6 marks)
i. CHAID
ii. CART
iii. Data Warehouse
b. Discuss any ethical concerns over data ware housing and data mining. (5 marks)
c. Discussany three reasons why there is need to standardize knowledge discovery
process models. (9 marks)
3. a. Provide a typical application of On-Line Analytical Processing in business (4 marks)
b. Discuss the four categories of data mining techniqus/tools according to Keating, 2008. (8 marks)
c. Using typical examples, illustrate any two new business opportunities
that data mining technology can generate. (8 marks)
4. a. Differentiate between data mining and knowledge discovery process. (4 marks)
b. Provide a detailed descriptin of the six-step CRISP-DM Model. (6 marks)
c. Analyze the most commonly used techniques in data mining. (10 marks)
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