Unit 7



1)      What is cluster? Briefly describe the categories of clustering techniques (or)

 Describe how categorization of major clustering methods is being done.

2)      What is density based clustering? Describe DBSCAN clustering algorithm.

3)      What is cluster analysis explain the type of cluster analysis.

4)      Describe the dissimilarity measures for interval scaled variables and binary variables.

5)      Describe about conceptual clustering?

6)      What is partitioning method? Describe K-means clustering algorithm.

7)      What is cluster analysis? Explain partitioning methods.

8)      What is cluster analysis? Explain Density based methods

9)      What is Grid based clustering? Describe any one Grid based clustering algorithm.

10)  What is hierarchical clustering? Describe any one Hierarchical clustering algorithm.

11)  What is clustering and what is conceptual clustering? Describe dimensions and measures in a spatial data cube?

12)  Briefly describe the dissimilarity measures for categorical, ordinal and ration-scaled variables.

13)  Briefly describe the dissimilarity measures for interval-scaled variables and binary variables.

14)  What is partitionaing method? Describe any one partitioning based clustering algorithm.

15)  What is partition method? Explain in detail.

16)  Describe how categorization of major clustering methods is done.

17)  Give an example of how specific clustering methods may be integrated, for example, where one clustering algorithm is used as a preprocessing step for another.

18)  Use a diagram to illustrate how, for a constant Min Pts value, Density- based cluster with respect to a higher density are completely contained in density connected sets obtained with respect to a lower density.

19)  Explain the following

                    a) DBSCAN

                     b) OPTICS

                     c) DENCLUE

                      d) BIRCH

20) Write algorithms for k-means and k-medoids. Explain

21) Explain outlier analysis in detail.

22) Discuss about types of data in cluster analysis.

23) Explain Grid- based methods

24) Discuss about binary variables, DENCLUE and DBSCAN.

 25) Explain model based clustering methods in detail.

26) What are the categories of major clustering methods? Explain

 27) What is distance based outlier? What are efficient algorithms for mining distance based algorithm? How are outliers determined in this method?

28) Given the following measure for the variable age:


                 i)Compute the mean absolute deviation of age

                 ii) Compute the Z-score for the first four measurements.

29) Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8):

                  i) Compute the Euclidean distance between the two objects.

                  ii) Compute the manhattan distance between two objects.

                  iii) Compute the minkowski distance between the two objects, using q=3

30) Explain about statistical based outlier detection and deviation based outlier detection

31) What is data normalization? Explain any two normalization methods.

32) Categorize major clustering methods.

33) what is outlier? Why is it important? Briefly discuss about statistical based outlier detection.

34) Given the following measurement for the variable age:


 Standardize the variable by the following:

                i) Compute the mean absolute deviation of age

                ii) Compute the z-score for the first four measurements. 



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    March 2012