Data Warehousing and Mining — Winter 2024

What is Data Warehousing? Explain its features.

List out issues in data mining and describe any two.

What is Data Mining? Explain KDD process.

Compare OLAP and OLTP systems.

Draw the three tier architecture of data warehouses.

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

What is an ‘Outlier’? How do Outliers impact the results of Mining? List out various methods to detect Outliers and explain any one in brief.

Why pre-processing is required in data mining process?

List out the various noise removing methods and explain any one in detail.

Explain the steps of the Apriori algorithm for mining frequent item sets with candidate generation.

List out various Concept Hierarchy and explain any one.

List out various methods for data discretization and explain any one in detail.

What is data transformation? Explain the different data transformation approaches for transforming data.

Why tree pruning useful in decision tree induction?

Write a short note on text mining.

Explain Linear and Non-linear Regression.

Discuss dimensionality reduction in brief.

What is feature selection and explain methods for feature selection.

Explain Naïve Bayesian classification in detail with example.

Discuss k-NN classification algorithm.

How k-Mean clustering method differs from k-Medoids clustering Method?

What is the confusion matrix, and how is it used to evaluate a classifier? OR1

List out various issues regarding Classification and prediction and describe any one in brief.

Define Fact Table and dimension table.

List out various types of Web Mining and explain any two in detail.