DATA MINING IV Year B.Tech. CSE I - Sem JNTUH (R18)

Unit 1: Data Mining:

1. A supermarket wants to analyze customer purchase patterns. Which type of data (structured, unstructured, semi-structured) would be most helpful, and why?

2. Compare and contrast data mining with data warehousing. How can data mining benefit from a pre-existing data warehouse?

3. Outline the four phases of a typical data mining process. Explain the key tasks involved in each phase.

4. Imagine you need to develop a system to recommend movies to users based on their past viewing history. What data mining task primitives would be crucial for this project?

5. Discuss the ethical considerations involved in mining customer data. How can data privacy be protected while still extracting valuable insights?

Unit 2: Association Rule Mining:

1. Explain how the Apriori algorithm identifies frequent itemsets in a transaction database. What are its limitations, and how can they be addressed?

2. Given a set of association rules with different support and confidence values, how would you choose the most interesting and potentially profitable ones?

3. Describe a real-world scenario where association rule mining could be used to improve marketing campaigns or customer service strategies.

4. Compare and contrast FP-Growth with Apriori for frequent pattern mining. How does FP-Growth achieve better efficiency?

5. Explain the concept of sequential pattern mining. How can it be used to predict customer behavior or event sequences?

Unit 3: Classification:

1. Describe how decision trees work for classifying data points. Explain the advantages and disadvantages of using decision trees as classifiers.

2. Compare and contrast k-nearest neighbors and Naive Bayes as classification algorithms. In which scenarios would you prefer one over the other?

3. How can model evaluation metrics like accuracy, precision, recall, and F1-score help you analyze the performance of your classification model?

4. Describe two techniques for improving the performance of a classification model, such as feature engineering or cross-validation.

5. Give an example of a real-world application where classification plays a crucial role in decision-making or prediction.

Unit 4: Clustering and Applications:

1. Explain the k-means clustering algorithm and its objective function. How do you determine the optimal number of clusters for a given dataset?

2. Compare and contrast hierarchical clustering with k-means. How can hierarchical clustering be used to discover more complex cluster structures?

3. Explain how density-based clustering algorithms like DBSCAN can handle outliers and noise in the data.

4. Discuss the challenges of clustering high-dimensional data. What are some techniques for dimensionality reduction in data mining?

5. Give an example of how clustering can be used for customer segmentation in a marketing campaign or for anomaly detection in network traffic.

Unit 5: Advanced Concepts:

1. Explain the challenges and limitations of mining data streams. How can data mining algorithms be adapted to handle continuously arriving data?

2. Describe two time-series analysis techniques you could use for forecasting future trends or identifying anomalies in temporal data.

3. How can spatial data mining be used to analyze geographical data and discover patterns between locations? Give an example application.

4. Explain how text mining techniques can be used to extract keywords, topics, and sentiment from unstructured textual data.

5. Discuss the potential applications of web mining in search engine optimization and social network analysis. What are the ethical considerations involved?