RGTU IT-840 DATA MINING AND WAREHOUSING PAPERS I RGTU IT 8TH SEM OLD PAPERS I RGTU IT-840 DATA MINING OLD PAPERS I RGTU DATA MINING & WAREHOUSING OLD EXAM PAPERS I RGPV DATA MINING & WAREHOUSING QUESTION PAPERS I RGPV DATA MINING & WAREHOUSING SAMPLE PAPERS I RGPV DATA MINING & WAREHOUSING LAST YEAR PAPERS I DATA MINING & WAREHOUSING EXAM PAPERS I RGPV DATA MINING & WAREHOUSING MODEL PAPERS I DATA MINING & WAREHOUSING GUESSING PAPERS I RGTU IT-840 DATA MINING & WAREHOUSING GUESS PAPERS I RGPV DATA MINING & WAREHOUSING SAMPLE TEST PAPERS I RGPV DATA MINING & WAREHOUSING QUESTION PAPERS I RGPV DATA MINING & WAREHOUSING MODEL PAPERS I RGPV DATA MINING & WAREHOUSING PREVIOUS YEAR PAPERS I RGPV DATA MINING & WAREHOUSING MODEL TEST PAPERS I RGTU DATA MINING & WAREHOUSING OLD EXAM PAPERS I RGTU DATA MINING & WAREHOUSING EXAM PAPERS I RGTU DATA MINING & WAREHOUSING OLD QUESTION PAPERS I RGTU MCA DATA MINING & WAREHOUSING PAPERS I RGTU MCA DATA MINING & WAREHOUSING SAMPLE PAPERS
Posted by Er. AMIT DWIVEDI , , , ,
R.G.P.V. Bhopal (MP)
I.T.(8th Semester) EXAM,Dec-2010
DATA MINING & WAREHOUSING [IT-840]
Time: 3Hrs     Max Marks: 100     Min Marks: 35
Note:Attempt Any 5 questions.All question Carry equal marks.
Q.1. Write short notes on the following:
             (i) Cluster analysis
            (ii) Outlier analysis
            (iii) Evolution analysis
            (iv) Classification and prediction

Q.2. Consider some data to apply data smoothing techniques.
            (i) Smoothing by bin
            (ii) Smoothing by bin median
            (iii) Smoothing by bin boundaries
            (iv) How might you determine outliers in the data

Q.3..(a) Explain various concept hierarchies with examples.?
         (b) Define the following:
                  (i) Simplicity
                 (ii) Certainty
                 (ii) Utility
                 (iv) Novelty.

Q.4. Attribute oriented induction generates one or a set of generalized descriptions. How can
these descriptions be visualized? Explain in detail.?

Q.5. How can you improve the efficiency of "apriori based mining" with the help of the
following:
                 (i) Hash based technique.
                (ii) Transaction reduction   
                (iii) Partitioning
                (iv) Sampling 
                (v) Dynamic item set counting.

Q.6.(a) Compare the advantages and disadvantages of eager classification (eg: decision tree) versus lazy classification ( eg: K-nearest neighbor).?
       (b) What is case based learning?
 
Q.7.(a) How is CLARANS different from CLARA? Illustrate this using a small example.?
       (b) What is the main principle in partitioning methods of clustering?

Q.8.(a) Which frequent item set mining is suitable for text mining?
       (b) What are the differences between mining techniques of structured data and unstructured data and semi structured data?
 

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