DATA MINING IV Year B.Tech. IT I - Sem JNTUH R-18

1. Differentiate between structured, semi-structured, and unstructured data and discuss its impact on data mining techniques.

2. Explain the various functionalities of data mining, including knowledge discovery, prediction, and description.

3. Compare and contrast different types of interestingness measures used in data mining tasks.

4. Describe the architecture of a typical data mining system and its integration with a data warehouse.

5. Discuss the major challenges encountered in data mining, such as data quality, dimensionality, and scalability.

6. Explain the different data preprocessing techniques (cleaning, integration, transformation, reduction) and their importance in data mining.

Unit - II: Association Rule Mining

1. Define frequent itemsets and explain the Apriori algorithm for mining them from large datasets.

2. Describe the differences between positive and negative associations and discuss various methods for mining them.

3. Compare and contrast correlation analysis and association rule mining, highlighting their strengths and limitations.

4. Explain constraint-based association mining and its applications in specific domains like market basket analysis.

5. Discuss the concept of graph pattern mining and its use in finding complex relationships within data.

Unit - III: Data Classification

1. Differentiate between classification and prediction in data mining. Explain the basic concepts involved in classification tasks.

2. Explain the decision tree induction algorithm for building classification models, including entropy and information gain concepts.

3. Compare and contrast Bayesian classification and rule-based classification techniques.

4. Give examples of lazy learner algorithms in classification and discuss their advantages and disadvantages.

5. Explain the importance of evaluation metrics like accuracy, precision, recall, and F1-score in assessing the performance of classification models.

Unit - IV: Clustering and Applications

1. Define cluster analysis and discuss the different types of data suitable for clustering tasks.

2. Explain the major categories of clustering methods: partitioning, hierarchical, density-based, and grid-based algorithms.

3. Compare and contrast K-means and DBSCAN clustering algorithms, highlighting their strengths and weaknesses.

4. Discuss outlier analysis and its importance in identifying anomalies and noise in data.

5. Give examples of real-world applications of clustering in various domains like customer segmentation, image segmentation, and gene expression analysis.

Unit - V: Advanced Concepts

1. Explain the challenges and techniques involved in mining data streams with continuous flow.

2. Discuss the special considerations and algorithms for mining time-series data, such as forecasting and trend analysis.

3. Describe the concept of mining sequential patterns in transactional databases and its applications in market basket analysis.

4. Explain the challenges and techniques for mining object-oriented data, spatial data, multimedia data, and text data.

5. Give examples of applications for spatial data mining in areas like geographic information systems and urban planning.