**NRJ/KW/17/4626
Max. Marks- 80**

**Notes : 1. All questions carry marks as indicated.**

**2. Solve Question 1 OR Questions No. 2.**

**3. Solve Question 3 OR Questions No. 4.**

**4. Solve Question 5 OR Questions No. 6.**

**5. Solve Question 7 OR Questions No. 8.**

**6. Solve Question 9 OR Questions No. 10.**

**7. Solve Question 11 OR Questions No. 12.**

**8. Due credit will be given to neatness and adequate dimensions.**

**9. Assume suitable data whenever necessary.**

**10. Illustrate your answers whenever necessary with the help of neat sketches.**

1. a) Explain KDD in detail with neat diagram. ** [08 M]**

b) Explain Data integration & Transformation in Data mining. ** [06 M]**

**OR**

2. a) Discuss major issues in Data mining. ** [07 M]**

b) Explain applications of data mining in detail. ** [07 M]**

3. a) Define data warehouse. Explain an architecture of Data warehouse with suitable diagram. ** [07 M]**

b) Briefly explain the OLAP guidelines suggested by Dr. Codd. ** [06 M]**

**OR**

4. a) Differentiate between OLTP and OLAP. ** [07 M]**

b) Explain in detail life cycle of Data warehouse. ** [06 M]**

5. a) What do you mean by mining frequent patterns, Association & correlation with an example.

** [06 M]**

b) Explain constraint- Based mining with suitable example. ** [07 M]**

**OR**

6. a) What is correlation Analysis? ** [04 M]**

b) Define the following terms. ** [09 M]**

i) Association mining.

ii) Frequent item sets.

iii) Closed item sets.

7. a) Explain support vector machine with suitable diagram. ** [06 M]**

b) Discuss different issues related to classification & prediction. ** [07 M]**

**OR**

8. a) Explain classification by Decision Tree Induction with an example. ** [07 M]**

b) Write short note on Back propagation. ** [06 M]**

9. a) What is clustering? Why it is required. ** [03 M]**

b) Differentiate between K-means and K -medoids. ** [06 M]**

c) What is outlier? Why outlier mining is important. ** [05 M]**

**OR**

10. a) Explain any two clustering methods with their types in detail. ** [14 M]**

11. a) Explain the techniques for mining time – series data. ** [07 M]**

b) Define the following terms. ** [06 M]**

i) Data stream.

ii) Time series Data.

iii) Sequence Data.

**OR**

12. a) Write short note on any three. ** [13 M]**

i) Graph mining.

ii) Link mining.

iii) Social Network Analysis.

iv) Multi relational Data Mining

.

Login

Accessing this course requires a login. Please enter your credentials below!