APRIL-2011 SEZ6A / SEE6A
PART-A
1. What is data mining?
2. Mention the kinds of patterns that can be mined.
3. Define : a) star b) snowflake.
4. Give the syntax for task relevant data specification.
5. What is meant by concept description?
6. How to measure the dispersion of data?
7. Give an example for association rule mining.
8. How to check the redundant multilevel association rules?
9. What is the need for classification?
10. Give the statement of Bayes theorem.
11. List the types of data involved in cluster analysis.
12. Give the expansion for BIRCH.
PART-B
13. Explain the concept of outlier analysis.
14. Mention the differences between operational database systems and data warehouse.
15. Mention the data mining tasks and explain.
16. How to design graphical user interface based on a data mining query language?
17. Explain the techniques of mining frequent item sets without candidate generation.
18. What is meant by back propagation and interpretability?
19. How wavelet transformation can be used for clustering?
PART-C
20. Discuss the following in data reduction?
a) Data cube aggregation
b) Data compression.
21. Why do perform attribute relevance analysis? Discuss the methods involved in it.
22. How to mine the quantitative association rules and distance based association rules?
23. Explain the techniques for estimating classifier accuracy. Is accuracy enough to judge a classifier?
24. Discuss the following in density-based methods
a) A density based clustering method based on connected regions with sufficiently high density.
b) Ordering points to identify the clustering structure.
NOVE-2011 SEE6A/SEZ6A
PART-A
1. Give the importance of data warehouse.
2. Define classification.
3. What are the factors to be considered in data transformation?
4. Name the primitives to be considered in defining a data mining query.
5. What is concept description?
6. When the classes can be comparable?
7. Define:
a) Occurrence frequency of an item set
b) Frequent item set.
8. What is meant by ice berg querry?
9. Name the criteria needed for comparing and evaluating classification and prediction methods.
10. Define class conditional independence in Bayesian classification.
11. What are the different types of data that will be involved in cluster analysis?
12. What is meant by ware cluster?
PART-B
13. Discuss on any five major issues in data mining.
14. Compare on-line transaction processing with on-line analytical processing (on any five features)
15. Give the syntax for specifying the kind of knowledge to be mined. Explain with examples.
16. What is meant by data generalization? Discuss the approaches meant for it.
17. Discuss the concept of a decision tree with an example.
18. Show with an example that strong rules are not necessarily interesting.
19. How does clustering in Quest Works?
PART-C
20. Discuss on the implementation concept of data ware house.
21. Describe the mining descriptive statistical measures in large databases.
22. How to mine single dimensional Boolean Association rules from transactional databases?
23. State Bayes theorem. Discuss on Bayesian classification.
24. Discuss cluster analysis through density – based methods.
APRIL-2012 SEE6A /SEZ6A
PART-A
1. What is data mining?
2. What is Data mining Engine?
3. Define: Text database.
4. What is concept description?
5. Define: Classification.
6. Define: Quantitative Association Rules.
7. What is multilevel Association Rule?
8. Define: Fazzy Set.
9. What is pruning?
10. What is Data Cleaning?
11. Define Density Based Method.
12. What is Grid Based Method?
PART-B
13. Explain the various Data mining Functionalities.
14. Explain about Data Integration and Transformation.
15. Write about Data mining Query Language and concept description.
16. Explain the Multilevel Association Rule from transaction databases.
17. Explain the genetic Algorithm and Rough set approach.
18. Write a note on Linear regression and nonlinear regression.
19. Write about interval- scaled variables and binary variables.
PART-C
20. Explain in detail about Advanced Data and information systems.
21. Explain the General procedures for Mining class comparisons.
22. Discuss in detail about Mining Multidimensional Association Rules and Mining Quantitative Association rules.
23. Explain the classifier Accuracy Measures and Predictor Error Measures.
24. Describe the various requirements of Clustering in Data Mining.
NOVEMBER 2012 51318/SEE6A/SEZ6A
PART-A
1. Define: Data mining.
2. Define: Data Transformation.
3. What is Hetogeneous database?
4. What is data generalization?
5. Define: full meterialization.
6. What is frequent pattern growth?
7. What is constraint-based rule mining?
8. What is classification?
9. Define : Black propagation.
10. What is support vector machine?
11. What is cluster analysis?
12. Define: Data matrix.
PART-B
13. Explain about data streams and multimedia database.
14. Explain about missing values and noisy data.
15. Write about data mining query language and concept description
16. Write about single dimensional boolean association rules.
17. Write a note on ‘bayes theorem’.
18. Explain about k-Nearest-Neighbour classifiers and case-based reasoning.
19. Explain the classical partitioning methods.
PART-C
20. Describe the various components of data mining system.
21. Explain the data generalization and summarization.
22. Discuss in detail about mining quantitative association rules.
23. Explain in detail about back propagation algorithm.
24. Describe on agglomerative and divisive hierarchical clustering.
APRIL-2013 SEE6A/SEZ6A
PART-A
1. Define Data mining
2. List out any two types of database.
3. What are the roles of Classification of data mining?
4. What is called cleaning.
5. What is the goal of data mining?
6. What is the classification tree?
7. Define the concept of classification.
8. What is Decision tree?
9. What do you mean by Cluster Analysis?
10. Write the issues relating to the diversity of database types.
11. What is confidence?
12. What are the different types of data used for cluster analysis?
PART-B
13. Explain Text database and multimedia databases in detail.
14. Describe challenges to data mining regarding data mining methodology and user interaction issues.
15. What are the different processes in Knowledge discovery in databases?
16. Explain about mining Multilevel Association Rules.
17. Explain the clustering process in data mining
18. Explain Decision tree induction.
19. Explain about Data Integration and Transformation.
PART-C
20. Briefly explain about various Database systems
21. Explain Model Based Clustering.
22. Explain mining multidimensional Association Rules from Relational Database and data warehouses.
23. Describe Predictive data mining. What are the different hierarchical choices in the Data mining.
24.Explain various types of data in cluster analysis.
APRIL-2013 SEE6H/SEZ6H
PART-A
1. Define : Data Mining.
2. Write a note on: Data Integration
3. List the names of Data Smoothing Techniques.
4. What are Metarules?
5. Define : Set-grouping Hierarchy.
6. What is Concept Description?
7. What is Association Rules Mining?
8. Define: Frequent Itemset.
9. What is a Decision Tree?
10. Write a note on: Bayes Theorem.
11. What are Genetic Algorithms?
12. What is Dissimilarity Matrix?
PART-B
13. Write short notes on : Data mining Functionalists.
14. Discuss briefly on: Preprocessing the Data.
15. Write short notes on: Data Transformation.
16. Discuss briefly about a data mining query language with example queries.
17. How to generate Association Rules from Frequent Itemsets? Explain with example.
18. What are the Issues Regarding classification and Prediction? Explain.
19. Write the steps in K-means algorithms.
PART-C
20. Explain about different strategies for Data Reduction.
21. Explain the Architectures of data mining systems.
22. Explain multilevel Association Rules from transactions databases.
23. Describe about classifier accuracy.
24. Explain any one Grid-Based method.
NOVE-2013 SEE6H/ SEZ6H
PART-A
1. Define: Data Warehouse.
2. What is meant by data Cleaning?
3. Define: Association Analysis.
4. What are Schema hierarchies?
5. Define: Data Generalization?
6. Write the form of a quantitative discriminant rule.
7. Define: Frequent closed Itemset.
8. What is an Iceberg query?
9. What are multidimensional association rules?
10. What are Bayesian classifiers?
11. Define: Cluster analysis.
12. What are agglomerative hierarchical clustering methods?
PART-B
13. Write short notes on: classification of data mining system.
14. Discuss briefly about data reduction.
15. What are data mining primitives? Explain.
16. What are the differences between concept description in large databases and online analytical processing?
17. Discuss the basic concepts in association rule mining.
18. Write short notes on: Decision tree induction.
19. Discuss briefly on : Any ONE density based methods.
PART-C
20. Explain about data Preprocessing.
21. Describe in detail, data generalization and summarization.
22. Explain about single dimensional Boolean association rules from transaction databases.
23. Discuss in detail, classification based on concepts from association rules mining
24. Explain the different types of data in cluster analysis.
NOVE-2013 SEE6A/SEZ6A
PART-A
1. What is Data warehouse?
2. Define: Temporal Data base.
3. What is Spatial database?
4. What is Data Cube?
5. What is Concept Comparison?
6. What is Apriori Algorithm?
7. What is Constraint-based rule mining?
8. Define: Predication.
9. What is Rough set?
10. What is Cluster?
11. What is Rule-based classifier?
12. What is Minkowski distance?
PART-B
13. Explain on Spatial database and Spatiotemporal databases.
14. Explain about Classification and Data Warehouse.
15. Write about Data Generalization and Architectures of Data mining systems.
16. Explain the various multidimension Association rules from data warehouse.
17. Explain the various issues regarding classification and prediction.
18. Write a note on "Information gain and Gain ratio".
19. Write about Ordinal and Categorical variable in detail.
PART-C
20. Describe the process of knowledge discovery.
21. Explain the various mining class comparisons in detail.
22. Discuss in detail about mining multidimentional association rules from relational databases and mining quantitative Association rules.
23. Explain the following a) Gini Index b) Tree Pruning
24. Describe on Categorization of major clustering methods.
APRIL-2014 SEE6H/SEZ6H
PART-A
1. What is Data Mining?
2. Define Data Cleaning.
3. What defines a data mining task?
4. Give the syntax for characterization of the kind of knowledge to be mined.
5. What is concept description?
6. Name any two ways of classifying association rules.
7. Mention the basic idea of sampling.
8. What is Apriori?
9. Name any two applications of classification and prediction.
10. List any four criteria by which classification and prediction methods can be compared and evaluate.
11. Define Clustering.
12. Give the expansion for DBSCAN.
PART-B
13. How data mining helps in the process of knowledge discovery?
14. How the information from data warehouse help the organisations?
15. How to perform attribute relevance analysis for concept description.
16. Mention the differences between concept description in large databases and online analytical processing.
17. Explain the technique of improving the efficiency of Apriori algorithm.
18. Mention the basic strategy for Decision tree induction.
19. How to compute the dissimilarity between object described by either symmetric or asymmetric binary variables?
PART-C
20. Explain the concept of Data Reduction.
21. Describe the architectures of Data Mining Systems.
22. Discuss the methods for mining multilevel association rules involving items at different levels of abstraction.
23. How to estimate the classifier accuracy?
24. Describe the statistical approach for model based clustering methods.
APRIL-2014 SEE6A/SEZ6A
PART-A
1.Define Genetic algorithm.
2. List out any two types of database.
3. Differentiate between Data Mining and Data Warehousing.
4. What do you mean by cluster Analysis?
5. What is Hierarchical method?
6. Define the concept of classification.
7. What is the goal of data mining?
8. Describe issues relating to the diversity of database types.
9. Define Predictive model.
10. What is a classification tree?
11. List the types of data in cluster analysis.
12. What are the roles of classification of data mining?
PART-B
13. Explain the Architecture of a typical data mining system.
14. Explain Text databases and multimedia databases in detail.
15. Give few techniques to improve the efficiency of apriori algorithm.
16. What are the different processes in knowledge discovery in databases?
17. Explain about k-means and k-medoids.
18. Explain the clustering process in Data Mining.
19. Explain any two Data Preprocessing techniques.
PART-C
20. Explain Model Based Clustering.
21. Explain about Data Mining Functionalists.
22. Describe Predictive data Mining. What are the different hierarchical choices in the Data Mining?
23. Explain Bayesian Classification.
24. Explain GRID Based clustering method.
APRIL-2015 SEE6A / SEZ6A
PART-A
1. What is meant by Data mining?
2. Name any two issues to be considered during data integration.
3. What defines a data mining task?
4. Define tight coupling.
5. What are the differences between concept description in large databases and online analytical processing?
6. List any two ways by which the association rules can be classified.
7. Name any two ways by which the efficiency of Apriori may be improved.
8. What are the three common binning strategies available?
9. How prediction is viewed?
10. What are Bayesian classifiers?
11. Define internal- scaled variables.
12. How to improve the quality of hierarchical clustering?
PART-B
13. Discuss on the architecture of Data Mining System.
14. Explain the technique involves in data smoothing.
15. What are the primitive for specifying a data mining task?
16. How to perform attribute relevance analysis for concept description?
17. How to generate association rules from frequent item sets.
18. How Naive Bayesian classifier works?
19. List the steps the outline the spirit of the Clustering using Representatives (CURE) algorithm.
PART-C
20. Discuss the concept of data reduction.
21. What is data generalization? with examples, discuss on attribute - oriented induction.
22. How multilevel association rules can be mined efficiently using concept hierarchies?
23. What is a decision tree? Discuss the algorithm for decision tree induction.
24. What are the requirements needed for clustering in data mining?