Data Mining — Winter 2024

Define following terms : 1. Data warehouse 2. Starnet Query model 3. BIRCH

Differences between operational database systems and Data Warehouses.

Explain the basic concepts of classification.

What are some typical OLAP (Online Analytical Processing) operations performed in a data warehouse?

What is the process of extraction, transformation, and loading (ETL) in a data warehouse?

What is data mining, and why is it important in data analysis? What types of data can be mined in data mining processes?

Describe decision tree and How does tree pruning contribute to improving decision tree models?

What is decision tree induction and What are attribute selection measures, and why are they important in decision tree induction?

Short Note on : Rule-based Classification.

Describe the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) multiphase hierarchical clustering approach with clustering feature trees.

Explain density-based methods such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and OPTICS (Ordering Points To Identify the Clustering Structure) in outlier analysis.

How does the Apriori algorithm work in mining frequent itemsets?

Define market-basket analysis and explain its significance in data mining. Discuss the basic concepts of frequent itemsets and closed itemsets in the context of market-basket analysis.

Explain Bayes' Theorem and its application in Naïve Bayesian classification.

Describe the bootstrap technique in model evaluation.

What is decision tree induction, and how is it used in classification?

What is cluster analysis, and what are its requirements?