Dataware Housing and Data Mining ElectiveII Question Papers Andhra University

 Dataware Housing and Data Mining ElectiveII

Question Papers Andhra University 

B. Tech (CSE) Degree Examination

Forth Year – Second Semester

DATAWARE HOUSING AND DATA MINING (ELECTIVE-II)

Effective from the admitted batch of 2004-2005

Time: 3 hrs
Max Marks: 70

First Question is Compulsory

Answer any four from the remaining questions

All Questions carry equal marks

Answer all parts of any question at one place

1. Briefly discuss.
a. Types of OLAP
b. Correlation analysis for handling redundancy.
c. Discretization
d. Ice-berg query.
e. Constraint -based rule mining
f. Non-linear regression
g. Ordinal variables for cluster analysis.

2. a. What is data mining? Briefly describe the components of a data mining system.
b. What kinds of patterns can be identified in a data mining system?

3. a. Write the differences between operational database and data warehouse.
b. Briefly describe 3-tier Data warehouse architecture

4. a. Write different approaches to data transformation.
b. Propose an algorithm in pseudo-code for automatic generation of a concept hierarchy for categorical data based on the number of distinct values of attributes in the given schema.

5. a. Discuss the essential features of a typical data musing query language like DMQL.
b. Consider association Rule below, which was mined from the student database at Big University:
Major(X. “science”) &rarr status (X,”undergrad”).

Suppose that the number of students at the university (that is, the number of task-relevant data tuples) is 5000. that 56% of undergraduates at the university major in science, that 64% of the students are registered in programs leading to undergraduate degrees, and that 70% of the students are majoring in science
a. Compute the confidence and support of above rule
b. Consider Rule below
Major (X,”biology”) → status (X,”undergrad”). [17%,80%]
Suppose that 30’% of science students are majoring in biology. Would you consider Rule 2 to be novel with respect to rule1? Explain.

6. a. Discuss why attribute relevance analysis is needed and how it can be performed.
b. Outline a data cube-based incremental algorithm for mining analytical class comparisons.

7. Write the A priori algorithm for discovering frequent item sets for mining single-dimensional Boolean Association Rule and discuss various approaches to impiove its efficiency.

8. a. Discuss the backpropagation algorithm for neural network-based classification of data.
b. What are the different categoiies of clustering methods?

 

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