Data Warehousing and Mining — Summer 2025

Compare data mart and data warehouse.

Adata warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data – Justify.

What is Cuboid? Explain various OLAP operations on data cube with example.

Differentiate Fact table vs. Dimension table.

briefly explain classification and prediction.

Explain the KDD process in detail.

Explain the major issues in data mining.

Briefly discuss the need for data preprocessing.

Explain the following terms with suitable example. 1) Data Integration 2) Data Transformation

Draw the diagram and describe the architecture of a data mining system.

Explain parametric and non-parametric methods of data reduction.

What is data cleaning? How to handle the missing value in data cleaning?

What is noise? Describe the possible reasons for noisy data. Explain the different techniques to remove the noise from data.

Briefly explain Linear and Non-linear regression.

What is market basket analysis? Explain the two measures of rule interestingness: support and confidence.

Explain the steps of the Apriori Algorithm for mining Frequent Itemsets with Candidate Generation. Use a suitable example to illustrate your answer.

Discuss : training and test dataset.

What is classification? Explain classification as a two-step process with a diagram.

Explain how the accuracy of a classifier/predictor can be measured.

Explain text mining using example.

Write a short note on tree pruning.

Explain the working of the k-Means clustering algorithm.

Write a note on web mining.

Explain the following as attribute selection measures: (i) Information Gain (ii) Gain Ratio.

Discuss Bayesian classification.