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Name of the College : Turbomachinery Institute of Technology &Sciences (TITS)
University : JNTU Hyderabad
Department : Computer Science Engineering
Subject Name : Data Warehousing and Data Mining
Degree : B.Tech
Year/Sem : III/I
Website : tits.ac.in
Document Type : Question Bank

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TITS Data Warehousing & Mining Question Paper

UNIT-I

1. (a) Briefly discuss the data smoothing techniques.
(b) Suppose that the data for analysis include the attribute age. The age values for the data tuples are (in increasing order):13,15,16,16,19,20,20,21,22,22,25,25,25,25,30,33,33,35,35,35,35,36,40,45,46, 52,70.

Related / Similar Question Paper : TITS B.Tech Information Security Question Bank

i. Use smoothing by bin means to smooth the above data, using a bin depth of 3. Illustrate your steps. Comment on the effect of the technique for the given data.
ii. How might you determine outliers in the data?
iii. What other methods are there for data smoothing? [R07, Dec,2011, 16M, set 2]

2. Briefly discuss the Discretization and concept hierarchy techniques. [R07, Dec,2011, 16M, set 4]

3. Write short note on the following architectures of data mining systems:
(a) No coupling (b) Loose coupling (c) Semitight coupling (d) Tight coupling.[R07, Dec, 2011, 16M, set 3]

4. (a) List and describe any four primitives for specifying a data mining task.
(b) Write about Semitight coupling and Loose Coupling. Differentiate them. [R07, Dec, 2011, 16M, set 4]

5. (a) How can you go about filling in the missing values in data cleaning process?
(b) Discuss the data smoothing techniques. [R07, Dec, 2011, 16M, set 1]
6. Explain the architecture of a typical data mining system. [R07, Dec,2011, 8M, set 4]
7. Write the syntax for the following data mining primitives:
(a) The kind of knowledge to be mined.
(b) Measures of pattern interestingness. [R07, Dec,2011, 8M, set 2]
8. Explain various data reduction techniques. [R05, Dec,2011, 16 M, set 2]
9. (a) Briefly discuss the forms of Data preprocessing with neat diagram.
(b) Explain about concept hierarchy generation for categorical data. [R05, March,2010, 8+8M, set 1]
10. (a) Draw and explain the architecture of typical data mining system.
(b) Differentiate OLTP and OLAP. [R05, March,2010, 8+8M, set 2]
11. Briefly discuss the Discretization and concept hierarchy techniques. [R05, March,2010, 16M, set 2]
12. Explain data mining as a step in the process of knowledge discovery. [R05, March,2010, 8M, set 3]
13. Suppose that the data for analysis include the attribute age. The age values for the data tuples are (in increasing order): 13,15,16,16,19,20,20,21,22,22,25,25,25,25,30,33,33,35,35,35,35,36,40,45,46,52,70.
(a) What is the mean of the data? (b) What is the median? (c) What is the mode of the data? Comment on the data’s modality. (d) What is the mid range of the data? (e) Can you find (roughly) the first quartile(Q1),and third quartile(Q3) of the data? (f) Give the five number summaries of the data. (g) Show a box plot of the data. (h) How is the quantile-quantile plot different from a quantile plot? [R05, March,2010, 16M, set 3]
14. (a) Given the following measurement for the variable age: 16, 25, 28, 46, 29, 44, 38, 37, 54, 27 Standardize the variable by the following: i. Compute the mean absolute deviation of age. ii. Compute the Z-score for the first four measurements. [R05, March,2010, 4+4 M, set 3]
15. (a) How can we smooth out noise in data cleaning process? Explain.
(b) Why preprocessing of data is needed? [R05, March,2010, 4+4 M, set 4]

UNIT – II

1. (a) Explain data mining as a step in the process of knowledge discovery.
(b) Differentiate operational database systems and data warehousing. [R07, Dec,2011, 16M, set 2]
2. (a) Explain the design and construction process of data warehouses.
(b) Explain the architecture of a typical data mining system. [R07, Dec,2011, 16M, set 4]
3. Briefly discuss about the following data warehouse implementation methods: (a) Indexing OLAP data
(b) Metadata Repository. [R07, Dec,2011, 16M, set 1]
4. Explain the design and construction process of data warehouses. [R07, Dec,2011, 8M, set 4]
5. (a) Explain data mining as a step in the process of knowledge discovery. (b) Differentiate operational database systems and data warehousing. [R05, Dec,2011, set 2; March,2010, 8 +8 M, set 1]