Unit I

  1. Explain Data mining as a step in the process of knowledge discovery.
  2. Draw and explain the architecture of typical data mining systems.
  3. Differentiate OLTP and OLAP.
  4. What is data mining and data warehousing? Give their applications.
  5. Briefly discuss the functionalities of data mining.
  6. Briefly discuss about Multidimensional data model
  7. Multidimensional Schema.
  8. Architecture of data mining systems
  9. Briefly discuss about data warehouse architecture
  10. Classification of data mining systems

Unit II

  1. Briefly discuss the forms of data processing with neat diagram.
  2. Explain about concept hierarchy generation for categorical data.
  3. Explain various data reduction techniques.
  4. Explain about concept hierarchy generation for numerical attributes.
  5. Explain about data Integration and Transformation techniques
  6. Briefly explain about discritization and Concept Hierarchy Generation for numerical and Categorical data.
  7. Briefly explain about needs for preprocessing data.
  8. Explain various data cleaning techniques.

Unit II I                                              

  1. List and describe data mining primitives for specifying a Data Mining Task.
  2. Briefly discuss about Task-relevant data specifications
  3. Explain the syntax for  Task-relevant data specifications.
  4. Describe why concept hierarchies are useful in data mining.
  5. Briefly explain about Data Mining Query Language with suitable examples
  6. Explain about designing graphical user interfaces based on Data Mining query Language.

Unit IV

  1. What is concept description and explain about Attribute relevance analysis for data characterization
  2. What are the differences between concept description in large databases and OLAP?
  3. Differentiate between predictive and descriptive data mining
  4. State and explain algorithm for attribute oriented induction.
  5. Explain mining class comparisons using example.
  6. Explain various formats for presenting derived generalized relations.
  7. Explain various mining descriptive statistical measures in large databases

Unit V

  1. Discuss about mining frequent item sets without candidate generation.
  2. What is association rule mining? Discuss about multilevel association rule mining from transactional databases in detail.
  3. Write the FP-growth algorithm. Explain.
  4. What is Iceberg query? Explain with example..
  5. Discuss about ARCS.
  1. Explain mining multidimensional  association rules from relational databases and warehouses
  2. What is correlation analysis? And explain constraint based association mining


Unit VI

  1. How scalable is decision tree induction? Explain.
  2. Describe working procedure of simple Bayesian classifier.
  3. Write backpropagation algorithm and explain.
  4. Discuss about nearest neighbor classifiers and case-based reasoning.
  5. Can any ideas from association rule mining be applied to classification? Explain.
  6. Explain about prediction and Explain Bayesian belief Networks
  7. How does tree pruning work? What are some enhancements to basic decision tree induction?
  8. what is classification and explain classification by Decision Tree Induction

Unit VII

  1. What is cluster analysis? What are the various types of data in Cluster Analysis? Explain.
  2. Given two objects represented by the tuples (22, 1,42,10) and (20,0,36,8):
    1. Compute the Euclidean distance between the two objects
    2. Compute the Euclidean distance between the two objects
    3. Compute the Euclidean distance between the two objects
  3. Explain categorization of Major Clustering Methods
  4. what is distance based Outlier? What are the efficient algorithms for mining distance-based algorithm? How are outliers determined in this method?
  5. Given following measures for variable age:

18,22,25,42,28,43,33,35,56,28 Standardize the variable by the following

i. Compute the mean absolute deviation of age.

ii. Compute the Z-Score for the first four measurements.

  1. Describe Model based clustering methods.
  2. Suppose that the data mining task is to cluster the following eight points ( with (x,y) representing location) into three clusters.

A1(2,10), A2(2,5),A3(8,4),B1(5,8),B2(7,5),B3(6,4),C1(1,2),C2(4,9).

The distance function is Euclidean distance. Suppose initially we assign A1,B1 and  C1 as the center of each cluster, respectively. Use the k-means algorithm to show only

1.      the three cluster centers after the first round execution

2.      the final three clusters

  1. Explain DBSCAN algorithm with suitable example.
  2. How does CLIQUE work.
  3. Explain Partitioning Methods
  4. Explain Density-Based Methods
  5. Explain Grid-based Methods
  6. Explain Model-Based Clustering Methods



  1. Explain the construction of spatial data cube with suitable example.
  2. Explain methods are for information retrieval? Explain
  3. Describe web usage mining.
  4. Explain construction and mining of object cubes
  5. What is multimedia database? Explain mining multimedia databases.
  6. What is time series database? What is a sequence database? Explain mining time series and sequence data.
  7. Define spatial database, multimedia database, time series database, sequential database and text database.
  8. Explain Periodicity analysis and Latent semantic indexing
  9. Explain mining associations in multimedia data
  10. Briefly discuss about  Multidimensional Analysis and Descriptive Mining of Complex Data Objects
  11. Briefly discuss about Mining Spatial Databases
  12. Briefly discuss about Multimedia Databases
  13. Briefly discuss about Mining Time-series and Sequence Data Mining
  14. Briefly describe about Text Databases
  15. Briefly discuss about mining the World Wide Web.

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