Code No: R05321204

R05

Set No. 2

in

III B.Tech II Semester Supplementary Examinations,May 2010 DATA WAREHOUSINGANDDATA MINING Information Technology Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ????? 1. Explain the following:

(b) Mining Time-series and sequence data.

ld .

(a) Mining the Text databases

[8+8]

2. (a) Describe the challenges to data mining regarding performance issue.

or

(b) What are the differences between the three main types of data warehouse usage: Information processing, Analytical processing, and data mining? Discuss the motivation behind OLAP mining. [8+8] 3. (a) Give the algorithm to generate a decision tree from the given training data. (b) Explain the concept of integrating data warehousing techniques and decision tree induction.

uW

(c) Describe multilayer feed-forward neural network.

[8+4+4]

4. Which algorithm is used for discovering frequent item sets without candidate generation? Explain with an example. [16] 5. (a) Briefly describe the Interestingness measure specification. (b) Explain the syntax for Interestingness measure specification.

[8+8]

nt

6. 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.

Aj

(a) 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.

(b) How might you determine outliers in the data? (c) What other methods are there for data smoothing? [16]

7. Write short notes for the following in detail: (a) Measuring the central tendency (b) Measuring the dispersion of data. 1

[16]

Code No: R05321204

R05

Set No. 2

8. (a) Define nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods.

[2+2+2+10]

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nt

uW

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in

?????

2

Code No: R05321204

R05

Set No. 4

in

III B.Tech II Semester Supplementary Examinations,May 2010 DATA WAREHOUSINGANDDATA MINING Information Technology Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ????? 1. Explain the following:

(b) Mining Time-series and sequence data.

ld .

(a) Mining the Text databases

[8+8]

or

2. 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) 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. (b) How might you determine outliers in the data?

uW

(c) What other methods are there for data smoothing?

[16]

3. Write short notes for the following in detail: (a) Measuring the central tendency

(b) Measuring the dispersion of data.

[16]

nt

4. Which algorithm is used for discovering frequent item sets without candidate generation? Explain with an example. [16] 5. (a) Briefly describe the Interestingness measure specification. [8+8]

Aj

(b) Explain the syntax for Interestingness measure specification.

6. (a) Describe the challenges to data mining regarding performance issue. (b) What are the differences between the three main types of data warehouse usage: Information processing, Analytical processing, and data mining? Discuss the motivation behind OLAP mining. [8+8]

7. (a) Give the algorithm to generate a decision tree from the given training data. (b) Explain the concept of integrating data warehousing techniques and decision tree induction. (c) Describe multilayer feed-forward neural network. 3

[8+4+4]

Code No: R05321204

R05

Set No. 4

8. (a) Define nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods.

[2+2+2+10]

Aj

nt

uW

or

ld .

in

?????

4

Code No: R05321204

R05

Set No. 1

in

III B.Tech II Semester Supplementary Examinations,May 2010 DATA WAREHOUSINGANDDATA MINING Information Technology Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ????? 1. Which algorithm is used for discovering frequent item sets without candidate generation? Explain with an example. [16]

ld .

2. (a) Define nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods. 3. Explain the following: (a) Mining the Text databases

or

(b) Mining Time-series and sequence data.

[2+2+2+10]

[8+8]

4. (a) Briefly describe the Interestingness measure specification.

(b) Explain the syntax for Interestingness measure specification.

[8+8]

uW

5. (a) Give the algorithm to generate a decision tree from the given training data. (b) Explain the concept of integrating data warehousing techniques and decision tree induction. (c) Describe multilayer feed-forward neural network.

[8+4+4]

6. Write short notes for the following in detail: (a) Measuring the central tendency

[16]

nt

(b) Measuring the dispersion of data.

7. (a) Describe the challenges to data mining regarding performance issue.

Aj

(b) What are the differences between the three main types of data warehouse usage: Information processing, Analytical processing, and data mining? Discuss the motivation behind OLAP mining. [8+8]

8. 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) 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. (b) How might you determine outliers in the data? 5

Code No: R05321204

R05

Set No. 1

(c) What other methods are there for data smoothing? [16]

Aj

nt

uW

or

ld .

in

?????

6

Code No: R05321204

R05

Set No. 3

in

III B.Tech II Semester Supplementary Examinations,May 2010 DATA WAREHOUSINGANDDATA MINING Information Technology Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ?????

ld .

1. 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) 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.

or

(b) How might you determine outliers in the data?

(c) What other methods are there for data smoothing?

[16]

2. (a) Briefly describe the Interestingness measure specification.

uW

(b) Explain the syntax for Interestingness measure specification.

[8+8]

3. (a) Describe the challenges to data mining regarding performance issue. (b) What are the differences between the three main types of data warehouse usage: Information processing, Analytical processing, and data mining? Discuss the motivation behind OLAP mining. [8+8]

nt

4. Which algorithm is used for discovering frequent item sets without candidate generation? Explain with an example. [16] 5. (a) Give the algorithm to generate a decision tree from the given training data. (b) Explain the concept of integrating data warehousing techniques and decision tree induction.

Aj

(c) Describe multilayer feed-forward neural network.

[8+4+4]

6. Explain the following: (a) Mining the Text databases

(b) Mining Time-series and sequence data.

[8+8]

7. (a) Define nominal, ordinal, and ratio-scaled variables. (b) Discuss about Classical partitioning methods. 8. Write short notes for the following in detail: 7

[2+2+2+10]

Code No: R05321204

R05

Set No. 3

(a) Measuring the central tendency (b) Measuring the dispersion of data.

[16]

Aj

nt

uW

or

ld .

in

?????

8

R05 Set No. 2

2. III B.Tech II Semester Supplementary Examinations,May 2010 ... [8+8] · 3. (a) Give the algorithm to generate a decision tree from the given training data.
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