Data Mining — Summer 2025

Define what a Data Warehouse is and explain its key characteristics.

Describe the multi-tiered architecture of Data Warehousing.

Briefly outline the processes involved in Extraction, Transformation, and Loading (ETL) and the role of the Metadata Repository.

Explain the different models of Data Warehousing, including Enterprise Warehouse, Datamart, and Virtual Warehouse.

Define Data Mining and discuss its importance in today’s data-driven environment.

Discuss the applications targeted by Data Mining, particularly in Business Intelligence and Web Search Engines.

Describe the types of data that can be mined and the various patterns that can be identified through Data Mining.

Define Market-Basket Analysis and explain its significance in understanding consumer behavior.

Explain the Apriori algorithm for Frequent Itemset Mining, detailing its process and importance.

Discuss how association rules are generated from frequent itemsets and the significance of this process.

Define Classification and its importance in data mining and predictive analytics.

Explain Bayesian Classification, focusing on Bayes’ Theorem and the Naïve Bayesian Classification approach.

Discuss Rule-based Classification, detailing how IF-THEN rules are used and the processes of Rule Extraction and Rule Induction using a Sequential Covering Algorithm.

Define Cluster Analysis and explain its significance in data mining and pattern recognition. What are the essential requirements for effective cluster analysis?

Provide an overview of basic clustering methods, including the main characteristics of Partitioning Methods such as K-Means and K-Medoids.

Compare and contrast Hierarchical Methods, specifically Agglomerative versus Divisive clustering, and describe the process of Hierarchical Clustering.

Explain various Distance Measures used in clustering algorithms and discuss advanced methods like BIRCH and Chameleon for hierarchical clustering.