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CS2032 DATA WAREHOUSING AND DATA MINING
SURYA GROUP OF INSTITUTIONS
SCHOOL OF ENGINEERING & TECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE&ENGINEERING
ACADEMIC YEAR 2011-2012 / ODD SEMESTER
SUBJECT CODE\SUBJECT NAME: CS2032 \ DATA WAREHOUSING AND DATA MINING
YEAR/SEM: IV/VII
UNIT-5-CLUSTERING AND APPLICATIONS AND TRENDS IN DATA MINING
PART A (2 MARKS)
1. What is a cluster?
2. What are the two data structures in cluster analysis?
3. List some of the clustering algorithms.
4. What is cluster analysis?
5. Distinguish between clustering and classification.
6. What is the objective function of the k-means algorithm?
7. Mention the advantages of hierarchical clustering.
8. Define dendrogram representation of hierarchical clustering.
9. Define relative interconnectivity.
10. Define relative closeness.
11. What major advantages does DENCLUE have in comparison with other clustering
algorithms?
12. Define category utility.
13. Define feature subset selection.
14. Define subspace clustering.
15. Why does CLIQUE confine its search for dense units of higher dimensionality to the
intersection of the dense units in the subspaces?
16. How effective is CLIQUE?
17. Define constraint-based cluster analysis.
18. Define the classifications of semi-supervised clustering.
19. Define semi-supervised clustering.
20. Define outlier analysis.
21. What is outlier analysis?
22. Define LOF.
23. List the some applications of data mining.
24. Write down the applications of data mining.
PART B
1. Describe about the types of data in cluster analysis.
2. Explain about the categorization of major clustering methods.
3. What is clustering? Briefly describe the different kinds of clustering methods with
examples in each case.
(16) & (16)
4. Discuss the different types of clustering methods. (8)
5. What is cluster analysis? Discuss how clustering techniques are classified. (8)
CS2032 DATA WAREHOUSING AND DATA MINING
6. What is clustering? How does it differ from classification? Describe the following
approaches to clustering methods, partitioning methods and hierarchical methods.
Give an example for each. (2+2+4+4+4=16)
7. Illustrate the strength and weakness of k-means in comparison with k-mediods
algorithm.
(16)
8. Describe the working of PAM(Partitioning around mediods) algorithm.
(8)
9. With a relevant example discuss K-Means clustering technique. (8)
10. BIRCH and CLARANS are two interesting clustering algorithms that perform
clustering in large data sets.
a. Outline how BIRCH performs clustering in large data sets.
(10)
b. Compare and outline the major differences of the two scalable clustering
algorithms. (6)
11. Explain the following clustering methods in detail
(16)
a. BIRCH
b. CURE
12. Describe in detail about Density-Based clustering methods.
13. Describe in detail about Grid-Based clustering methods.
14. Describe in detail about Model-Based clustering methods.
15. Describe in detail about clustering high dimensional data.
16. Describe in detail about constraint-based cluster analysis.
17. Describe in detail about outlier analysis.
18. Discuss the major differences between classification and clustering techniques.(4)
19. Discuss the applications of data mining in business.
(8)
20. Discuss in detail the applications of data mining for financial data analysis? Give
suitable data flow diagram. (16)
21. Discuss in detail of application of data mining for biomedical and DNA data analysis
and telecommunication industry? (10)
22. Discuss in detail application of data mining for financial data analysis and the retail
industry.
(10)
23. Explain the role of data mining in financial data analysis.
(10)
24. List the applications and trends in data mining in detail. (16)
25. Write short notes on data mining for retail industry.
(8)
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