Data Warehousing and Mining – Unit 14 – Review Questions

Data Warehousing and Mining – Unit 14 – Applications and Trends in Data Mining

Data Mining Applications

1.  Write a short note on data mining for biomedical and DNA data analysis.
2. What are the applications of data mining for financial data  analysis?
3. Discuss the benefits of data mining for the telecommunication industry.

Data Mining System Products and Research Prototypes

1. Discuss the various forces which influence the decision to choose a data mining system.

Additional Themes on Data Mining

1. Write a short note on visual and audio data mining.
2.  Enumerate and discuss various statistical techniques and methos for data analysis.

Trends in Data Mining

1. Discuss and elaborate the current trends in data mining.

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Data Warehousing and Mining – Unit 13 – Review Questions

Data Warehousing and Mining – Unit 13 – Architectures of Data Mining Systems

1. What are the characteristics of a desired architecture for data mining systems?
2. Discuss the following coupling schemes for a DB/DW/DM system:
   1. No coupling
   2. Loose coupling
   3. Semitight coupling
   4. Tight coupling
3.  Discuss the merits of the different coupling schemes for a DB/DW/DM system.

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Data Warehousing and Mining – Unit 12 – Review Questions

Data Warehousing and Mining – Unit 12 – Data Mining Query Language

1. Discuss the importance of a data mining query language.
2. What are the challenges faced in the design of an effective data mining query language?
3.  With a suitable example elaborate how DMQL can be used to specify task-relevant data for an association relationship.
4. Illustrate with an example the application of DMQL for specifying the kind of knowledge to be mined.
5. What is a meta pattern? How can it be used in DMQL to mine association rules?
6. Present DMQL for specifying a concept hierarchy.
7. Discuss the DMQL syntax for pattern interestingness measures.
8. How can DMQL be used to specify the forms of presentation and visualization used in displaying the discovered patterns?

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Data Warehousing and Mining – Unit 11 – Review Questions

Data Warehousing and Mining – Unit 11 – Data Mining Primitives

1. Discuss how a data mining query can be defined in terms of data mining primitives.
2. Enumerate the different primitives for specifying a data mining task.
3. What is task-relevant data? Explain. How is it related to a minable view?
4. Dicuss in brief the kind of knowledge to be mined and its determination of the data mining function.
5. Define the following terms:
   1. Concept hierarchies
   2. Schema hierarchies
   3. Set-grouping hierarchies
   4. Operation-derived hierarchies
   5. Rule-based hierarchies
6. Explain and discuss the following interestingness measures:
   1. Simplicity
   2. Certainty
   3. Reliability
   4. Novelty
7. What is the importance of visualization of discovered patterns? Explain the role of presentation in pattern visualization.

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Data Warehousing and Mining – Unit 10 – Review Questions

Data Warehousing and Mining – Unit 10 – Web Mining

1. Explain in brief the classification of web data.
2. Write a short note on web mining taxonomy.
3. What is targeting? Explain with examples.
4. Explain the different activities of text mining.
5. What is a crawler? Distinguish between periodic and incremental crawlers.
6. Write a short note on focused crawling.
7. Write a short note on unfocused crawling.
8. Explain in brief the Context Focused Crawler approach. How does this differ from traditional crawlers?
9. What is semantic indexing? Explain in the context of harvest caching.
10. Write a short note on WebML.
11. Explain personalization in the context of web content mining. What are the three types of web page personalization?
12. What is page rank? Explain its role in web structure mining.
13. Present and explain in brief the HITS algorithm.
14. What is web usage mining? Explain the three activities in web usage mining.
15. Explain the usefulness of a session  in preprocessing of web usage logs.
16. What is a trie? Explain its use in web usage mining.
17. Write a short note on pattren discovery on clickstream data.
18. Present and explain the maximal frequent forward sequences algorithm.
19. Present and explain the Online Adaptive Traversal Patterns (OAT) algorithm.
20. Write a short note on pattern analysis.
21. Construct a trie for the string (A,B,A,C).
22. Construct a suffix tree for the string (A,B,A,C).
23. Given the following sessions, {(A,B,A,C),(C,B,D,F),(A,B,A)}, indicate the sequential patterns, forward sequences, and maximal frequent sequences assuming a minimum support of 30%. Assume each session occurs from a different user.

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