Data Warehousing and Data Mining — Summer 2025

Discuss the concept hierarchy.

Differentiate between OLTP and OLAP.

With the help of a neat diagram explain the 3-tier architecture of a data warehouse. warehouse.

Explain various features of a data warehouse.

Discuss possible ways for the integration of a Data Mining system with a Database or Data Warehouse system.

Explain Star, Snowflake, and Fact Constellation schemas of a data warehouse with a suitable example.

With the help of a suitable example, illustrate the OLAP operations: ‘drill-down,’ ‘roll-up,’ ‘slice,’ and ‘dice.’.

Explain Variance and Standard Deviation.

List and describe methods for handling missing values in data cleaning.

List and Explain Data Mining Task Primitives.

Why data preprocessing is required?

Explain min-max and Z-score normalization.

Enlist data reduction strategies and explain any two.

Differentiate Clustering and classification.

Discuss Data Smoothing by Binning using a suitable example.

Discuss the key steps involved in the apriori algorithm for discovering frequent itemsets.

Explain Support and Confidence measures using proper example.

Compare supervised with unsupervised learning.

What are the limitations of the Apriori algorithm? Discuss any two methods to improve the efficiency of Apriori-based mining.

Explain web mining using example.

How does k-means clustering differ from k-medoids clustering?

Explain the concept of pruning in decision trees and its impact on model performance.

Explain Spatial mining using example.

Explain Linear regression with an example.

Explain k-Means clustering algorithm in detail.1