Data Mining — Summer 2023

Explain Stars, Snowflakes, and Fact Constellations Schemas.

Draw and explain the Data warehouse architecture.

Describe the steps involved in data mining when viewed as a process of knowledge discovery.

Explain research issues in Data Mining.

What is Data Mining? Explain Data Mining Process in detail.

Consider a transactional database where 1, 2, 3, 4, 5, 6, 7 are items. Suppose the minimum support is 60%. Find all frequent item sets using Apriori algorithm. ID ITEMS T_1 1, 2, 3, T_2 1, 2, 3, 4, T_3 1, 2, 3, T_4 1, 3, T_5 1, 2, 4, 5,

Define following terms & differentiate them: Data Mart, Enterprise Warehouse & Virtual Warehouse.

Discuss the variations of the Apriori algorithm to improve the efficiency.

Explain Classification and Regression for Predictive Analysis.

Why naïve Bayesian classification is called “naïve”? Briefly outline the major ideas of naïve Bayesian classification.

What do you mean by Random Forest Algorithm? Why is Random Forest Algorithm popular?

What are Outliers? Describe types of outliers also explain challenges of outliers detection.

List down the features of Bagged Trees. What are the Limitations of Bagging Trees?

Enlist the steps of K-Mean clustering algorithm. Explain it with suitable example.

Enlist the steps of ID3 decision tree generation algorithm. Explain it with suitable example and generate the tree.

Explain Clustering Methods in detail.

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