2. (a) Briefly discuss the forms of Data preprocessing with neat diagram. (b) Explain about concept hierarchy generation for categorical data.
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1 Code No: M0502/R05 Set No (a) Explain data mining as a step in the process of knowledge discovery. (b) Differentiate operational database systems and data warehousing. [8+8] 2. (a) Briefly discuss the forms of Data preprocessing with neat diagram. (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) Explain about analytical characterization. (b) Explain about concept comparison. [8+8] 5. (a) Explain about iceberg queries with example. (b) Can we design a method that mines the complete set of frequent item sets without candidate generation? If yes, explain with example. [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 status age salary count Sales Senior K...50K 30 Sales Junior K...30K 40 Sales Junior K...35K 40 Systems Junior K...50K 20 Systems Senior K...70K 5 Systems Junior K...50K 3 Systems Senior K...70K 3 Marketing Senior K...50K 10 Marketing Junior K...45K 4 Secretary Senior K...40K 4 Secretary Junior K...30K 6 Let salary be the class label attribute. Given a data sample with the values systems, junior;, and for the attributes department, status, and age, respectively, what would a naive Bayesian classification of the salary for the sample be? [16] 7. (a) Define nominal, ordinal, and ratio-scaled variables. 1 of 2
2 Code No: M0502/R05 Set No. 1 (b) Discuss about Classical partitioning methods. [ ] 8. (a) Explain spatial data cube construction and spatial OLAP. (b) Discuss about mining text databases. [8+8] 2 of 2
3 Code No: M0502/R05 Set No (a) Draw and explain the architecture of typical data mining system. (b) Differentiate OLTP and OLAP. [8+8] 2. Briefly discuss the Discretization and concept hierarchy techniques. [16] 3. Discuss about primitives for specifying a data mining task. [16] 4. (a) How is attribute relevant analysis for concept description performed? Explain. (b) Explain the limitations for class characterization. [8+8] 5. (a) Discus about Association rule mining. (b) What are the approaches for mining multilevel Association rules? Explain. [8+8] 6. (a) Describe the data classification process with a neat diagram. (b) Discuss about Bayesian classification. [8+8] 7. (a) What is Cluster Analysis? What are some typical applications of clustering? What are some typical requirements of clustering in data mining? (b) Define data matrix and dissimilarity matrix. Discuss about interval-scaled variables. [ ] 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] 1 of 1
4 Code No: M0502/R05 Set No (a) Explain data mining as a step in the process of knowledge discovery. (b) Differentiate operational database systems and data warehousing. [8+8] 2. Write a short note on following: (a) Missing Values. (b) Histogram analysis (c) Entropy-based discretization (d) Segmentation by natural partitioning. [16] 3. Write the syntax for the following data mining primitives: (a) Task-relevant data. (b) Concept hierarchies. [16] 4. Suppose that the data for analysis include the attribute age. The age values for the data tuples are (in increasing order): 13,15,16,16,19,20,20,21,22,22,25,25,25,25,30,33,33,35,35,35,35,36,40,45,46,52,70. (a) What is the mean of the data? (b) What is the median? (c) What is the mode of the data? Comment on the data s modality. (d) What is the mid range of the data? (e) Can you find (roughly) the first quartile(q1),and third quartile(q3) of the data? (f) Give the five number summaries of the data. (g) Show a box plot of the data. (h) How is the quantile-quantile plot different from a quantile plot? [16] 5. Explain the Apriori algorithm with example. [16] 6. What is Backpropagation? Explain Backpropagation classification. [16] 7. (a) Given the following measurement for the variable age: 16, 25, 28, 46, 29, 44, 38, 37, 54, 27 Standardize the variable by the following: 1 of 2
5 Code No: M0502/R05 Set No. 3 i. Compute the mean absolute deviation of age. ii. Compute the Z-score for the first four measurements. (b) Explain clustering using representatives algorithm with example. (c) Write an algorithm for DBSCAN and give an example of DBSCAN.[ ] 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] 2 of 2
6 Code No: M0502/R05 Set No (a) Discuss about Concept hierarchy. (b) Briefly explain about - classification of database systems. [8+8] 2. (a) How can we smooth out noise in data cleaning process? Explain. (b) Why preprocessing of data is needed? [8+8] 3. Discuss about primitives for specifying a data mining task. [16] 4. Write short notes for the following in detail: (a) Measuring the central tendency (b) Measuring the dispersion of data. [16] 5. (a) Write the FP-growth algorithm. Explain. (b) Discus about ARCS. [10+6] 6. (a) What is classification? What is prediction? (b) What is Bayes theorem? Explain about Naive Bayesian classification. (c) Discuss about k-nearest neighbor classifiers and case-based reasoning.[4+6+6] 7. (a) Use a diagram to illustrate how, for a constant MinPts value, density-based clusters with respect to a higher density (i.e., a lower value for ε, the neighborhood radius) are completely contained in density- connected sets obtained with respect to a lower density. (b) 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. [8+8] 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] 1 of 1
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