Download JNTUH MCA 4th Sem R13 2018 June-July 814BD Data Warehousing And Data Mining Question Paper

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Code No: 814BD

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

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Time: 3 Hours Max. Marks: 60

Note: This question paper contains two parts A and B.
Part A is compulsory which carries 20 marks. Answer all questions in Part A. Part B consists of 5 Units. Answer any one full question from each unit. Each question carries 8 marks and may have a, b, c as sub questions.

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PART - A 5 x 4 Marks =20

1. What are the characteristics of an interesting pattern? [4]
2. What is meant by multi dimensional data model? [4]
3. Give examples for a single dimensional association rule and a quantitative multidimensional association rules. [4]
4. What are the accuracy measures for a classifier? [4]

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5. List the merits and demerits of hierarchical agglomerative clustering. [4]

PART -B 5 x 8 Marks = 40

6. What is data mining? Explain it as a step in knowledge discovery process. [8]
   OR
   Demonstrate attribute subset selection as a preprocessing technique. [8]

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7. Define data warehouse. Compare it with database management systems. [8]
   OR
   Explain BUC algorithm for data-cube computation. [8]
8. Using FP Growth algorithm find frequent item sets(support threshold 30%) for the following data: [8]

   TID	List of Items
   1	Pen, eraser, marker, calculator, drafter
   2	Pencil, marker, eraser, cutter
   3	Pen, Pencil, eraser, A4 papers
   4	A4 papers, CD, marker
   5	Pencil, eraser, stapler, marker
   6	Pen, eraser, sharpener, calculator
   7	A4 papers, Pencil, eraser
   8	Calculator, drafter, Pen
   9	Pen, Pencil, CD, A4 papers.

   OR
   What is correlation analysis? Explain the significance of lift measure for correlation analysis. [8]

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9. How to prepare data for classification? Explain with suitable data set. [8]
   OR
   What are the characteristics of neural network that make a good classifier? Describe back propagation algorithm. [8]
10. Explain k-means algorithm and contrast it with k-medoid algorithm. [8]
    OR
    

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    What is an outlier? What is the need of outlier detection? Explain any one technique for outlier analysis. [8]

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