LOYOLA COLLEGE (AUTONOMOUS), CHENNAI 600 034
M.C.A. DEGREE EXAMINATION COMPUTER APPLICATIONS
FIFTH SEMESTER NOVEMBER 2017
CA 5807 DATA MINING
Date: 04-11-2017 Dept. No. Max. 100 Marks
Time: 09:00-12:00
Part-A
Answer ALL Questions (10 20
1. Compare descriptive and predictive mining
2. What is Data characterization?
3. Define Outliers
4. Give an example of Multidimensional association rule.
5. What is Data generalization?
6. Define Data transformation.
7. Compare Classification and Prediction.
8. What is multiple linear Regression analysis?
9. What are the requirements for clustering?
10. Write few applications of data mining in Telecommunications.
Part B
Answer ALL Questions 40)
11. What is Noise? Explain the techniques used to remove the noisy data.

Explain Attribute subset selection method for data reduction.
12. Explain the issues regarding classification and prediction.

Explain the Decision tree based classification in detail.
13. Explain k-means partitioning algorithm in Cluster Analysis.

Explain BIRCH clustering method in detail.
14. Write short notes on Multimedia data mining.

Write short notes on Mining WWW.
15. How to predict the class label for weather data set using Weka Tool.

Explain the Applications of Data mining in financial data analysis.
Part C
Answer any TWO Questions 20= 40)
16. Explain the architecture of Data mining in detail.
Explain the schema representation for multidimensional databases.
17. Describe Bayesian classification in detail.
Discuss Text data mining in detail.
18. Explain the social impacts of data mining.
A multilayer feed-forward neural network is given. Let the learning rate be 0.9. The
initial weight and bias values of the network are given in the following Table, along with
the first training tuple, X whose class label is 1. Compute the net input and
output of each unit and compute error of each unit.
An example of a multilayer feed-forward neural network.
Initial input, weight, and bias values are
x1 x2 x3 w14 w15 w24 w25 w34 w35 w46 w56
1 0 1 0.2 -0.3 0.4 0.1 -0.5 0.2 -0.3 -0.2 -0.4 0.2 0.1