Code: 9F00403
MCA IV Semester Supplementary Examinations May 2019
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DATA WAREHOUSING & MINING
(For 2009, 2010, 2011, 2012 (LC), 2013, 2014, 2015 & 2016 admitted batches only)
Time: 3 hours Max. Marks: 60
Answer any FIVE questions
All questions carry equal marks
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- What are the steps involved in the process of knowledge discovery? Explain.
- Mention the classification of data mining systems.
- According to William H what is data warehouse? Explain the key features.
- List and explain four different views regarding the design of a data warehouse.
- Write BUC algorithm for the computation of sparse or iceberg cubes.
- Write a note on descriptive data mining.
- Explain Apriori algorithm for discovering frequent item sets. How we can improve the efficiency of Apriori by hash based technique.
- Why is Naive Bayesian classification called naive?! Briefly outline the major ideas of naive Bayesian classification.
- What is clustering? Explain the requirements of clustering in data mining.
- Describe the different text mining tasks for extracted keywords or semantic information.
- Write a short note on spatial mining.
- List and explain challenging issues in data mining trends.
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