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

FirstRanker.com
Firstranker's choice
Code No: 814BD R13

--- Content provided by FirstRanker.com ---

--- Content provided by FirstRanker.com ---

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
MCA 1V Semester Examinations, January - 2018
DATA WAREHOUSING AND DATA MINING
Time: 3 Hours Max. Marks: 60

Note: This question paper contains two parts A and B.

--- Content provided by FirstRanker.com ---

PART - A
5 x 4 Marks =20

1. Discuss the data smoothing techniques.
2. Discuss the OLAP query processing.

--- Content provided by FirstRanker.com ---

3. Discuss constraint-based Association mining.
4. How does tree pruning work?
5. Why wavelet transformation useful for clustering?

PART -B
5 x 8 Marks = 40

--- Content provided by FirstRanker.com ---

1. a) Write the syntax for the following data mining primitives:
   a) The kind of knowledge to be mined.
   b) Measures of pattern interestingness. [4+4]
   OR
   b) Briefly discuss the data mining functionalities.

--- Content provided by FirstRanker.com ---

2. Briefly discuss the major issues in data mining regarding performance and diverse database types. [4+4]
   OR
3. a) Justify the role of data cube aggregation in data reduction process with an example.
   b) Differentiate operational database systems and data warehousing. [4+4]
   OR
   

   --- Content provided by FirstRanker.com ---

   a) What is data warehousing? Give their applications.
   b) Briefly discuss data warehouse architecture. [4+4]
4. a) Compare and contrast Apriori algorithm with frequent pattern growth algorithm. Consider a data set apply both algorithms and explain the results. [8]
   OR
   b) Explain how concept hierarchies are used in mining multilevel association rule?
   

   --- Content provided by FirstRanker.com ---

   Give the classification of association rules in detail. [4+4]
5. a) Discuss the five criteria for the evaluation of classification and prediction methods.
   b) Explain how rules can be extracted from training neural networks. [4+4]
   OR
   a) Explain the hold out method for estimating classifier accuracy.
   

   --- Content provided by FirstRanker.com ---

   b) Discuss Fuzzy set approach for classification. [4+4]
6. a) Write k-Means and k-Medoids algorithms.
   b) Explain COBWEB model. [4+4]
   OR
   a) Explain about Statistical-based outlier detection and Deviation-based outlier detection.
   

   --- Content provided by FirstRanker.com ---

   b) Given two objects represented by the tuples(22,1,42,10) and(20,0,36,8)
   i) Compute the Manhatten distance between the two objects.
   ii) Compute the Euchidean distance between the two objects. [4+4]

FirstRanker.com

--- Content provided by FirstRanker.com ---