Data Warehousing and Mining — Summer 2023

Explain various OLAP operations.

Compare Linear and nonlinear regression.

Explain Star, Snowflake and Fact constellation” schemas of data warehouse with suitable example. Q .2 (a ) Define the following terms: 0 1. OLAP 2. OLTP 3. OLAM

What is data mining integration in data warehousing? Explain with an example

Discuss data discretization and concept hierarchy generation.

Explain Naïve Bayesian classification in detail with example.

Define techniques to improve the efficiency of Apriori algorithm.

Define nominal and ordinal variables

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

What is feature selection in data mining?

Define Fact Table and dimension table.

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

Define Support & Confidence.

Discuss Issues regarding Classification and prediction

Describe and explain the different types of clustering methods.

What is outlier? Discuss different methods for outlier detection.

Explain the difference between a data warehouse and a data mart

What are the reasons for the presence of ‘noise’ in data collected for mining? Explain the methods to deal with noise.

Define data mart.

What is association rule mining? Explain with an example.

What is Decision Tree? Explain how classification is done using decision tree induction.

What is a data cube?

Discuss the limitations and challenges of data mining

Explain Web Mining in detail.