Questions
Syllabus
Qns. By Syllabus

Semester

Subject

Year

Tribhuwan University

Institute of Science and Technology

2076

Bachelor Level / Fourth Year / Seventh Semester / Science

B.Sc in Computer Science and Information Technology (CSC420)

(Data Warehousing and Data Mining)

Full Marks: 60

Pass Marks: 24

Time: 3 Hours

Candidates are required to give their answers in their own words as for as practicable.

The figures in the margin indicate full marks.

Section A

Long Answers Questions

Attempt any TWO questions.
[2*10=20]
1.
Do pattern and information refer to same aspect? Justify. Differentiate between data warehouse and operational database.[10]
2.
List the problems of Apriori algorithm with its possible solutions. Consider the following transaction dataset. What association rules can be found in this set, if the minimum support is 3 and the minimum confidence is 80%.

$\begin{array}{c|c} \text{Transaction_ID} & \text{Item_List} \\ \hline \text{T1} & \{K, A, D, B\} \\ \text{T2} & \{D, A, C, E, B\} \\ \text{T3} & \{C, A, B, E\} \\ \text{T4} & \{B, A, D\} \\ \end{array}$
[10]
3.
Discuss the types of web mining. Explain why K-means is sensitive to outlier and how does K-Medoid minimize this issue.[10]
Section B

Short Answers Questions

Attempt any Eight questions.
[8*5=40]
4.
How classification plays significance role in data mining? Explain. [5]
5.
Are the information given by data mining is always useful? What are the issues in data warehousing and data mining? [5]
6.
Explain the four characteristics of data warehouse. [5]
7.
Explain the optimization techniques in data cube computation. [5]
8.
How multidimensional data model helps in retrieving information? Explain with suitable example. [5]
9.
Compare the OLAP servers, ROLAP, MOLAP and HOLAP. [5]
10.
Give a syntax and example of data mining query language. [5]
11.
Differentiate between KDD and data mining. [5]
12.
What does data warehouse tuning mean? Describe the parameters. [5]
13.
Write short notes on (Any Two): a. Evolution analysis b. Decision trees c. Text mining d.Classification using Regression [5]