1. What is cluster analysis? What are the typical applications of cluster analysis? 2 to 8

2. What are the requirements of clustering in data mining? 9 to 13

3. What are the types of data that occur in cluster analysis and the way to

process them for analysis purposes?

4. Explain about interval‐scaled variables in cluster analysis. 18 to 23

5. Explain about binary variables in cluster analysis. 24 to 30

6. Explain about nominal, ordinal, and ratio‐scaled variables in cluster analysis. 31 to 38

7. Explain about mixed‐type variables in cluster analysis. 39 to 41

8. Briefly introduce the major clustering methods 42 to 51

9. Explain the centroid‐based k‐means method 52 to 59

10. Explain the variations of centroid‐based k‐means method 60 to 68

11. Explain about CLARA, a sampling‐based method 69 to 73

12. Write a short note on hierarchical clustering 74

13. Explain agglomerative and divisive hierarchical clustering 75 to 81

14. Explain the BIRCH method of hierarchical clustering 82 to 88

15. Explain about CURE – clustering 89 to 93

16. Explain about ROCK , a hierarchical clustering algorithm 94

17. Explain the dynamic modeling of clustering technique – chameleon 95 to 100

18. Explain about DBSCAN, a density based clustering method 101 to 109

19. Explain about the cluster analysis method – OPTICS 110 to 114

20. Explain about the density based clustering method – DENCLUE 115 to 120

21. Explain about STING, the grid‐based multiresolution clustering technique 121 to 127

22. Write about WaveCluster, a multiresolution clustering algorithm 128 to 130

23. Explain the CLIQUE (CLusering In QUEst) clustering algorithm that integrates

density‐based and grid‐based clustering

24. Explain different model‐bases statistical clustering methods 136 to 148

25. Explain different neural networks‐based clustering methods 149 to 152

26. Write a short note on outlier analysis including the causes of outliers and the

applications of outlier analysis

27. Explain how outliers are detected using the discordancy‐test and its variations 158

28. Write the outlines of algorithms for mining distance‐based outliers and

explain them

29. What is meant by deviation‐based outlier detection and explain the two

techniques of deviation‐based outlier techniques

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