R05321204-data-warehousing-and-data-mining

Set No. 1

Code No: R05321204

III B.Tech Supplimentary Examinations, Aug/Sep 2008 DATA WAREHOUSING AND DATA MINING (Information Technology) Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ⋆⋆⋆⋆⋆ 1. (a) Draw and explain the architecture of typical data mining system. (b) Differentiate OLTP and OLAP.

[8+8]

2. (a) Briefly discuss the data smoothing techniques. (b) Explain about concept hierarchy generation for categorical data.

[8+8]

3. (a) List and describe any four primitives for specifying a data mining task. (b) Describe why concept hierarchies are useful in data mining.

[8+8]

4. (a) How can we specify a data mining query for characterization with DMQL? (b) Describe the transformation of a data mining query to a relational query. [8+8] 5. Sequential patterns can be mined in methods similar to the mining of association rules. Design an efficient algorithm to mine multilevel sequential patterns from a transaction database. An example of such a pattern is the following “A customer who buys a PC will buy Microsoft software within three months”, on which one may drill down to find a more refined version of the patterns, such as “A customer who buys a Pentium PC will buy Microsoft office within three months”. [16] 6. Discuss about Backpropagation classification.

[16]

7. (a) What major advantages does DENCLUE have in comparison with other clustering algorithms? (b) What advantages does STING offer over other clustering methods? (c) Why wavelet transformation useful for clustering? (d) Explain about outlier analysis.

[3+3+3+7]

8. (a) Explain spatial data cube construction and spatial OLAP. (b) Discuss about mining text databases. ⋆⋆⋆⋆⋆

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[8+8]

Set No. 2

Code No: R05321204

III B.Tech Supplimentary Examinations, Aug/Sep 2008 DATA WAREHOUSING AND DATA MINING (Information Technology) Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ⋆⋆⋆⋆⋆ 1. (a) Explain data mining as a step in the process of knowledge discovery. (b) Differentiate operational database systems and data warehousing. 2. Explain various data reduction techniques.

[8+8] [16]

3. (a) Briefly discuss the various forms of Presenting and visualizing the discovered patterns. (b) Discuss about the objective measures of pattern interestingness.

[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) Which algorithm is an influential algorithm for mining frequent item sets for Boolean association rules? Explain. (b) What are additional rule constraints to guide mining? Explain.

[8+8]

6. The following table consists of training data from an employee database. The data have been generalized. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that below: Department Sales Sales Sales Systems Systems Systems Systems Marketing Marketing Secretary Secretary

status Senior Junior Junior Junior Senior Junior Senior Senior Junior Senior Junior

age salary count 31...35 46K....50K 30 26...30 26K...30K 40 31...35 31K...35K 40 21...25 46K...50K 20 31...35 66K...70K 5 26...30 46K...50K 3 41...45 66K...70K 3 36...40 46K...50K 10 31...35 41K...45K 4 46...50 36K...40K 4 26...30 26K...30K 6 Let salary be the class label attribute. Design a multilayer feed-forward neural network for the given data. Label the nodes in the input and output layers. [16] 7. (a) Define mean absolute deviation, z-score, city block distance, and minkowski distance. 1 of 2

Set No. 2

Code No: R05321204

(b) What are different types of hierarchical methods? Explain.

[2+2+2+2+8]

8. (a) Define spatial database, multimedia database, time-series database, sequence database, and text database. (b) What is web usage mining? Explain with suitable example. ⋆⋆⋆⋆⋆

2 of 2

[10+6]

Set No. 3

Code No: R05321204

III B.Tech Supplimentary Examinations, Aug/Sep 2008 DATA WAREHOUSING AND DATA MINING (Information Technology) Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ⋆⋆⋆⋆⋆ 1. (a) Explain the architecture of a typical data mining system (b) Discuss the issues regarding data warehouse architecture. 2. Explain various data reduction techniques.

[8+8] [16]

3. Write the syntax for the following data mining primitives: (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? Explain. (b) Explain the measures of central tendency in detail.

[8+8]

5. (a) Discus about Association rule mining. (b) What are the approaches for mining multilevel Association rules? Explain. [8+8] 6. (a) Explain decision tree induction classification. (b) Describe backpropagation classification.

[8+8]

7. (a) Discuss about binary, nominal, ordinal, and ratio-scaled variables. (b) Explain about grid-based methods.

[8+8]

8. A heterogeneous database system consists of multiple database systems that are defined independently, but that need to exchange transform information among themselves and answer global queries. Discuss how to process a descriptive mining query in such a system using a generalization-based approach. [16] ⋆⋆⋆⋆⋆

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Set No. 4

Code No: R05321204

III B.Tech Supplimentary Examinations, Aug/Sep 2008 DATA WAREHOUSING AND DATA MINING (Information Technology) Time: 3 hours Max Marks: 80 Answer any FIVE Questions All Questions carry equal marks ⋆⋆⋆⋆⋆ 1. Briefly compare the following concepts. Use an example to explain your points. (a) Snowflake schema, fact constellation, starnet query model. (b) Data cleaning, data transformation, refresh. (c) Discovery driven cube, multifeature cube, and virtual warehouse.

[16]

2. (a) Briefly discuss the data smoothing techniques. (b) Explain about concept hierarchy generation for categorical data.

[8+8]

3. (a) List and describe any four primitives for specifying a data mining task. (b) Describe why concept hierarchies are useful in data mining.

[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. Explain the Apriori algorithm with example.

[16]

6. (a) Can any ideas from association rule mining be applied to classification? Explain. (b) Explain training Bayesian belief networks. (c) How does tree pruning work? What are some enhancements to basic decision tree induction? [6+5+5] 7. (a) What are the categories of major clustering methods? Explain. (b) Explain about outlier analysis.

[6+10]

8. (a) How to mine Multimedia databases? Explain. (b) Define web mining. What are the observations made in mining the Web for effective resource and knowledge discovery? (c) What is web usage mining?

[10+4+2] ⋆⋆⋆⋆⋆

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