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# 2009 Osmania University BE Computer Engineering B.E. IV/IV (IT) I SEMESTER(New) (Main) Examination,Nov./Dec.,2009 -Data Warehousing and Data Mining (Elective-II) Question paper

 Course: BE Computer Engineering University/board: Osmania University

Faculty of Informatics

B.E. IV/IV (IT) I SEMESTER(New)(Main)Examination,Nov./Dec.,2009

Data Warehousing and Data Mining (Elective-II)

Time:3 Hours Max. Marks:75

Note:Answer all questions from Part-A.Answer any five questions from Part-B.

Part-A (Marks:25)

1. Define data mining and data warehouse.(Marks:2)

2. Define the following OLAP operations:(Marks:2)
(a)Rollup (b)Drilldown.

3. State the Bayes Theorem.(Marks:2)

4. Define the following types of Data in Cluster Analysis:(Marks:2)
(a)Categorical variable
(b)Ordinal variable

5. Define spatial mining and text mining.(Marks:2)

6. Define the following terms:(Marks:3)
(a)Binning
(b)Histogram Analysis

7. State the following terms:(Marks:3)
(a)Inter Quartile Range
(b)Five-Number Summary.

8. Define following terms:(Marks:3)
(a)Time Series Data
(b)Web Mining
(c)Multimedia Data Mining

9. Given two objects represented by the tuples
(22,1,42,10) and (20,0,36,8)
Compute the Euclidean distance between the two objects.(Marks:3)

10. Define any three Data Mining functionalities.(Marks:3)

PART-B (Marks:50)

11. (a) Explain data mining as a step in the process of knowledge discovery with diagram.(Marks:5)
(b)Explain major issues in Data Mining.(Marks:5)

12. (a)Write the differences between operational databases and data warehousing.(Marks:5)
(b)Explain normalization techniques in Data Transformation.(Marks:5)

13. (a)How does the Naive Bayesian classification work?Explain.(Marks:5)
(b)Explain classifier accuracy.(Marks:5)

14. (a)Explain about statistical-based outlier detection and deviation-based outlier detection
(Marks:5)
(b)Given two objects represented by the tuples
(23,4,56,6) and (30,0,26,10)
Compute the Minkowski distance between the two objects,using P=3.(Marks:5)

15. (a)Explain how to construct spatial Data Cube and spatial OLAP.(Marks:5)
(b)Explain Spatial Associations and co-location patterns.(Marks:5)

16. Explain how to mine the frequent items using Apriori Algorithm with example.(Marks:10)

17. (a)Write Algorithms for K-Means and K-Medoids.(Marks:5)
(b)Discuss about density based methods.(Marks:5)

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