Data Mining — Winter 2022

What is a Data Warehouse? Explain Differences between operational database systems and Data Warehouses.

Discuss following Data Warehousing Modeling: 1. Stars. 2. Snowflakes.

What is Data Mining; Why Data Mining? What kind of data can be mined? What Kind of Patterns Can be Mined?

Explain Knowledge Discovery (KDD) Process.

What is Data Pre-processing? Discuss Data Cleaning and Data Integration techniques.

What Is Frequent Pattern Analysis? Basic Concepts of Frequent Patterns and Association Rules

Prepare following Apriori Algorithm Database TDB Supmin = Tid Items 10 A, C, D 20 B, C, E 30 A, B, C, E 40 B, E

Explain Scalable Methods for Mining Frequent Patterns.

Discuss Challenges of Frequent Pattern Mining.

What is Classification & Prediction? Discuss Classification A Two-Step Process.

Discuss Decision Tree Induction. Also discuss Attribute Selection: Information Gain using Example.

Why Bayesian Classification? Discuss Naïve Bayesian Classifier using Training dataset.

Discuss following Techniques: 1. Bagging. 2. Boosting.

What is Cluster Analysis? Discuss Requirements of Clustering in Data Mining.

Discuss K-Means Clustering Method.

What are outliers? Types of outliers, Challenges of outlier Detection.

Short note on: BIRCH