JNTUK B.Tech R23 3-1 Data Warehousing & Data Mining subject CSE Branch imp questions

UNIT - I: Data Warehousing and Online Analytical Processing

2 Marks Questions:

1. Define data warehouse and explain its basic concepts.
2. What is OLAP and how does it differ from OLTP?
3. Explain data objects and attribute types in data mining.

5 Marks Questions:

1. Explain data warehouse modeling concepts including data cube and OLAP operations.
2. Describe data warehouse design principles and implementation strategies.
3. Discuss cloud data warehouse architecture and its advantages over traditional systems.
4. Explain data mining technologies, applications and major issues in pattern mining.
5. Describe basic statistical descriptions of data including measures of central tendency and dispersion.
6. Explain data visualization techniques and methods for measuring data similarity and dissimilarity.

UNIT - II: Data Preprocessing

2 Marks Questions:

1. What are the main steps involved in data preprocessing?
2. Define data cleaning and its importance in data mining.
3. What is data discretization and when is it used?

5 Marks Questions:

1. Explain data cleaning techniques for handling missing values, noisy data and inconsistent data.
2. Describe data integration process and challenges in combining data from multiple sources.
3. Explain various data reduction techniques including dimensionality reduction and numerosity reduction.
4. Describe data transformation methods including normalization, aggregation and discretization.
5. Compare different data preprocessing techniques and their applications in data mining.
6. Explain the complete data preprocessing pipeline with practical examples.

UNIT - III: Classification

2 Marks Questions:

1. What are the basic concepts of classification in data mining?
2. Define entropy and information gain in decision tree induction.
3. What is Bayes theorem and its application in classification?

5 Marks Questions:

1. Explain the general approach to solving classification problems with evaluation metrics.
2. Describe decision tree induction algorithm with attribute selection measures.
3. Explain tree pruning techniques and scalability issues in decision tree induction.
4. Describe Bayesian classification methods including Naïve Bayes classification with examples.
5. Explain rule-based classification techniques and their advantages over other methods.
6. Discuss model evaluation and selection techniques including cross-validation and performance metrics.

UNIT - IV: Association Analysis

2 Marks Questions:

1. Define frequent itemsets and association rules.
2. What is the difference between support and confidence in association rules?
3. Explain the Apriori property in frequent itemset generation.

5 Marks Questions:

1. Explain the problem definition of association analysis with market basket analysis example.
2. Describe frequent itemset generation techniques and the Apriori algorithm.
3. Explain rule generation process including confidence-based pruning methods.
4. Describe the complete Apriori algorithm for association rule mining with numerical examples.
5. Explain compact representation techniques for frequent itemsets.
6. Describe FP-Growth algorithm and compare it with Apriori algorithm.

UNIT - V: Cluster Analysis

2 Marks Questions:

1. What is cluster analysis and why is it important in data mining?
2. Define different types of clusters in clustering techniques.
3. What are the strengths and weaknesses of DBSCAN algorithm?

5 Marks Questions:

1. Explain the overview and basics of cluster analysis with different clustering techniques.
2. Describe the basic K-means algorithm with numerical examples and convergence criteria.
3. Explain additional issues in K-means clustering and bi-secting K-means algorithm.
4. Describe agglomerative hierarchical clustering algorithm with dendrogram construction.
5. Explain DBSCAN algorithm including density-based approach and parameter selection.
6. Compare K-means, hierarchical clustering and DBSCAN algorithms with their applications.