FACULTY OF INFORMATICS B.E. 3/4 (IT) II – Semester (New) (Main) Examination, May 2013 Subject : Data Warehousing and Data Mining (Elective – I) Time : 3 hours
Max. Marks : 75
Note : Answer all questions from Part-A. Answer any FIVE questions from Part-B. PART – A (25 Marks) 1. What is a data warehouse? And what is its use?
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2. State the purpose of data cleaning address the two issues in data cleaning.
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3. What is pattern-interestingness?
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4. Define frequent item set. How do you determine support and confidence?
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5. What is decision tree induction?
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6. What is a decision boundary in support vector machine?
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7. Define any two similarity measures used in cluster analysis.
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8. What is an outlier? Mention one use of an outlier.
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9. Define precision, recall and F-score.
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10. What is dimensionality reduction?
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PART – B (50 Marks) 11. Describe knowledge discovery process. 12. Explain the use of wavelet transforms in dimensionality reduction. 13. Sketch an integrated OLAM and OLAP architecture and explain its working. 14. Explain induction of a decision tree using information gain. 15. State k-means clustering algorithm and explain the process of clustering. 16. What are data streams? Explain random sampling method. 17. Write short notes on : a) Spatial data mining
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