Data Warehousing and Data Mining Model Question Paper

Data Warehousing and Data Mining (CSC-451)

B.Sc. CSIT 8^th Semester

Model Questions

Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate full marks.

Group A

Long Answer Questions (Attempt any TWO)                                                                                      [2×10=20]

1.       Suppose that a data warehouse for Big University consists of the following four dimensions: student, course, semester and instructor and two measures count and avg-grade. When at the lowest conceptual level (e.g. for a given student, course, semester and instructor combination), the avg-grade measure stores the actual course grade of the student. At higher conceptual levels avg-grade stores the average grade for the given combination.

aa.)      Draw a snowflake schema diagram for the data warehouse.

bb.)      Starting with base cuboid [student, course, semester, instructor] what specific OLAP operations (e.g. roll-up from semester to year) should one perform in order to list the average grade of CS course for each Big University Student.

cc.)       If each dimension has five levels (including all) such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

2.       A= {A1, A2, A3, A4, A5, A6}, assume σ=35% use a priori algorithm to get the desired solution.

A1	A2	A3	A4	A5	A6
0	0	0	1	1	1
0	1	1	1	0	0
1	0	0	1	1	1
1	1	0	1	0	0
1	0	1	0	1	1
0	1	1	1	0	1
0	0	0	1	1	0
0	1	0	1	0	1
1	0	0	1	0	0
1	1	1	1	1	1

3.       What kind of data preprocessing do we need before applying data mining algorithm to a data set. Explain data binning method to handle noisy data with example.

Group B

Short Answer Questions 

(Attempt any Eight-questions)                                                                               [8×5=40]

Question number 13 is compulsory.

4.       Explain the use of frequent item set generation process.

5.       Differentiate between data marts and data cubes.

6.       Explain OLAP operations with example.

7.       List the drawbacks of ID3 algorithm with over-fitting and its remedy techniques.

8.       Write the algorithms for K-means clustering. Compare it with k-nearest neighbor algorithm.

9.       What is text mining? Explain the text indexing techniques.

10.   Describe genetic algorithm using as problem solving technique in data mining.

11.   What do you mean by WWW mining? Explain WWW mining techniques.

12.   What is DMQL? How do you define Star Schema using DMQL?

13.   Write shorts notes (Any Two)

aa.)      Text Database mining

bb.)      Back propagation algorithm

cc.)       Regression

dd.)      HOLAP