Code No: M0522/R07
Set No. 1
IV B.Tech I Semester Supplementary Examinations, December 2013
DATA WAREHOUSING AND DATA MINING
(Computer Science & Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
⋆ ⋆ ⋆ ⋆ ⋆
1. (a) Discuss about Concept hierarchy.
(b) Briefly explain about - classification of database systems. [8+8]
2. Write a short note on following:
(a) Missing Values.
(b) Histogram analysis
(c) Noisy data removal
(d) Entropy-based discretization. [16]
3. (a) List and describe any four primitives for specifying a data mining task.
(b) Write about Semitight coupling and Loose Coupling. Differentiate them.
[8+8]
4. (a) Write and explain the basic algorithm for Attribute-oriented induction.
(b) Explain about the presentation of the derived generalization. [8+8]
5. Propose and outline a level shared mining approach to mining multilevel association
rules in which each item is encoded by its level position , and initial scan of the
database collects the count for each item at each concept level, identifying frequent
and sub frequent items. Comment on the processing cost of mining multilevel
associations with this method in comparison to mining single level associations.
[16]
6. (a) How is prediction different from classification? Explain Bayesian classification.
(b) Explain classifier accuracy. [8+8]
7. (a) Briefly discuss about density-based methods.
(b) Explain COBWEB model. [8+8]
8. (a) Discuss about multidimensional analysis and descriptive mining of complex data objects.
(b) Explain text data analysis and information retrieval. [8+8]
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Code No: M0522/R07
Set No. 2
IV B.Tech I Semester Supplementary Examinations, December 2013
DATA WAREHOUSING AND DATA MINING
(Computer Science & Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
⋆ ⋆ ⋆ ⋆ ⋆
1. (a) Describe the challenges to data mining regarding performance issue.
(b) What are the differences between the three main types of data warehouse us- age: Information processing, Analytical processing, and data mining? Discuss the motivation behind OLAP mining. [8+8]
2. (a) Briefly discuss the data smoothing techniques.
(b) Explain about concept hierarchy generation for categorical data.
3. Write the syntax for the following data mining primitives:
[8+8]
(a) Task-relevant data.
(b) Concept hierarchies. [16]
4. (a) What are the differences between concept description in large data bases and
OLAP?
(b) Explain about the graph displays of basic statistical class description. [8+8]
5. A database has four transactions. Let min-sup=60% and min-conf=80%.
TID Date items-bought
T100 10/15/99 {K, A, D, B}
T200 10/15/99 {D, A, C, E, B}
T300 10/19/99 {C, A, B, E}
T400 10/22/99 {B, A, D}
Find all frequent item sets using Apriori and FP-growth, respectively. Compare the efficiency of the two mining processes. [16]
[16] 6. Explain Decision tree induction classification.
7. (a) Briefly discuss about density-based methods.
(b) Explain COBWEB model. [8+8]
8. (a) Explain the classification and prediction analysis of multimedia data.
(b) What are basic measures for text retrieval? What methods are there for information retrieval?
(c) What is meant by ‘ authoritative ’ Web pages? Explain about mining the Web ’ s link structures to identify authoritative web page. [4+6+6]
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Code No: M0522/R07
Set No. 3
IV B.Tech I Semester Supplementary Examinations, December 2013
DATA WAREHOUSING AND DATA MINING
(Computer Science & Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
⋆ ⋆ ⋆ ⋆ ⋆
1. (a) Explain the major issues in data mining.
(b) Explain the three-tier datawarehousing architecture. [8+8]
2. Write a short note on following:
(a) Missing values
(b) Noisy data
(c) Inconsistent data
(d) Data cube aggregation.
3. Write the syntax for the following data mining primitives:
[16]
(a) The kind of knowledge to be mined.
(b) Measures of pattern interestingness. [16]
4. (a) How can we perform attribute relevant analysis for concept description? Ex- plain.
(b) Explain the measures of central tendency in detail. [8+8]
5. (a) Explain the following: i. Meta rule-guided mining of Association rules. ii. Mining guided by additional rule constraint.
(b) How might the efficiency of Apriori improved? Explain. [4+4+8]
6. (a) Write an algorithm for k-nearest neighbor classification given k and n, the number of attributes describing each sample.
(b) What is linear regression? Give an example of linear regression using the method of least squares. [8+8]
7. (a) Briefly discuss about density-based methods.
(b) Explain COBWEB model.
8. Explain the following:
(a) Mining spatial databases
(b) Mining the World Wide Web.
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[8+8]
[8+8]
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Code No: M0522/R07
Set No. 4
IV B.Tech I Semester Supplementary Examinations, December 2013
DATA WAREHOUSING AND DATA MINING
(Computer Science & Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
⋆ ⋆ ⋆ ⋆ ⋆
1. (a) What is data mining? What is data warehousing? Give their applications.
(b) Briefly discuss data warehouse architecture. [8+8]
2. (a) Briefly explain about the forms of Data preprocessing.
(b) Discuss issues to be considered during data integration process. [8+8]
3. Discuss about primitives for specifying a data mining task.
4. Write short notes for the following in detail:
(a) Measuring the central tendency
(b) Measuring the dispersion of data.
[16]
[16]
5. (a) Explain the following i. Mining distance based Association rules. ii. Multidimensional Association rules.
(b) What are additional rule constraints to guide mining? Explain.
7. (a) Define object-by-variable structure and object-by-object structure.
(b) Explain representative object-based technique.
(c) Write CURE algorithm and explain.
[4+4+8]
6. (a) Can any ideas from association rule mining be applied to classification? Ex- plain.
(b) Explain training Bayesian belief networks.
(c) How does tree pruning work? What are some enhancements to basic decision tree induction? [6+5+5]
[4+6+6]
8. (a) Explain the classification and prediction analysis of multimedia data.
(b) What are basic measures for text retrieval? What methods are there for information retrieval?
(c) What is meant by ‘ authoritative ’ Web pages? Explain about mining the Web ’ s link structures to identify authoritative web page. [4+6+6]
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