# Data Warehousing and Data Mining

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## Content

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Code No: R05410503

R05

Set No. 2

IV B.Tech I Semester Examinations,May/June 2012 DATA WAREHOUSING AND DATA MINING Computer Science And Engineering Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks

1. (a) Compare the advantages and disadvantages of eager classiﬁcation(e.g., decision tree, Bayesian, neural network) versus lazy classiﬁcation(e.g., k-nearest neighbor, case-based reasoning). (b) Can any ideas from association rule mining be applied to classiﬁcation? Explain. [8+8] 2. (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] 3. (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. 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? [16] 4. Explain the syntax for the following data mining primitives: (a) Task-relevant data (b) The kind of knowledge to be mined (c) Interestingness measures (d) Presentation and visualization of discovered patterns. 5. Explain the following: (a) Mining the Text databases (b) Mining Time-series and sequence data. 6. (a) Explain about constraint-based Association mining. (b) Give an example for Association rule mining. Classify Association rules.[8+8] 7. (a) Describe three challenges to data mining regarding data mining methodology and user interaction issues. 1 [8+8] [16]

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Code No: R05410503

R05

Set No. 2
[8+8]

(b) Draw and explain the Three-tier architecture of a data warehouse. 8. (a) Deﬁne nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods.

[2+2+2+10]

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Code No: R05410503

R05

Set No. 4

IV B.Tech I Semester Examinations,May/June 2012 DATA WAREHOUSING AND DATA MINING Computer Science And Engineering Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks

1. Explain the following: (a) Mining the Text databases (b) Mining Time-series and sequence data. 2. (a) Deﬁne nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods. 3. (a) Explain about constraint-based Association mining.

(b) Give an example for Association rule mining. Classify Association rules.[8+8] 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) Compare the advantages and disadvantages of eager classiﬁcation(e.g., decision tree, Bayesian, neural network) versus lazy classiﬁcation(e.g., k-nearest neighbor, case-based reasoning). (b) Can any ideas from association rule mining be applied to classiﬁcation? Explain. [8+8] 6. (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. 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? [16] 7. (a) Describe three challenges to data mining regarding data mining methodology and user interaction issues. (b) Draw and explain the Three-tier architecture of a data warehouse. 8. Explain the syntax for the following data mining primitives: (a) Task-relevant data 3 [8+8]

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[8+8]

[2+2+2+10]

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Code No: R05410503

R05

Set No. 4
[16]

(b) The kind of knowledge to be mined (c) Interestingness measures (d) Presentation and visualization of discovered patterns.

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www.jntuworld.com

www.jwjobs.net

Code No: R05410503

R05

Set No. 1

IV B.Tech I Semester Examinations,May/June 2012 DATA WAREHOUSING AND DATA MINING Computer Science And Engineering Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks

1. (a) Compare the advantages and disadvantages of eager classiﬁcation(e.g., decision tree, Bayesian, neural network) versus lazy classiﬁcation(e.g., k-nearest neighbor, case-based reasoning). (b) Can any ideas from association rule mining be applied to classiﬁcation? Explain. [8+8] 2. (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. 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? [16] 3. Explain the following:

(a) Mining the Text databases (b) Mining Time-series and sequence data. 4. Explain the syntax for the following data mining primitives: (a) Task-relevant data (b) The kind of knowledge to be mined (c) Interestingness measures (d) Presentation and visualization of discovered patterns. 5. (a) Explain about constraint-based Association mining. (b) Give an example for Association rule mining. Classify Association rules.[8+8] 6. (a) Deﬁne nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods. [2+2+2+10] [16]

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[8+8]

7. (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

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www.jntuworld.com

www.jwjobs.net

Code No: R05410503

R05

Set No. 1
[8+8]

8. (a) Describe three challenges to data mining regarding data mining methodology and user interaction issues. (b) Draw and explain the Three-tier architecture of a data warehouse.

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6

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www.jntuworld.com

www.jwjobs.net

Code No: R05410503

R05

Set No. 3

IV B.Tech I Semester Examinations,May/June 2012 DATA WAREHOUSING AND DATA MINING Computer Science And Engineering Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks

1. (a) Deﬁne nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods. 2. Explain the syntax for the following data mining primitives: (a) Task-relevant data (b) The kind of knowledge to be mined (c) Interestingness measures (d) Presentation and visualization of discovered patterns. [2+2+2+10]

3. (a) Compare the advantages and disadvantages of eager classiﬁcation(e.g., decision tree, Bayesian, neural network) versus lazy classiﬁcation(e.g., k-nearest neighbor, case-based reasoning). (b) Can any ideas from association rule mining be applied to classiﬁcation? Explain. [8+8] 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) Describe three challenges to data mining regarding data mining methodology and user interaction issues. (b) Draw and explain the Three-tier architecture of a data warehouse. 6. (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. 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? [16] 7. Explain the following: (a) Mining the Text databases 7 [8+8]

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[16]

www.jntuworld.com

www.jntuworld.com

www.jwjobs.net

Code No: R05410503

R05

Set No. 3
[8+8]

(b) Mining Time-series and sequence data. 8. (a) Explain about constraint-based Association mining.

(b) Give an example for Association rule mining. Classify Association rules.[8+8]

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