Dataware Housing & Mining (CY-604 (C)) - Important Questions

Explain Galaxy Schema for multidimensional databases in detail.

What is data warehouse? Explain the characteristics of data warehouse.

Explain Snowflake data warehouse schema in detail.

What is Vertical Partitioning and how it is done? Explain in detail.

Draw the data warehouse architecture and discuss its various components in detail.

Explain Data Warehouse implementation techniques in detail.

What is OLAP? Elaborate the concept of ROLAP with a neat diagram.

Explain the need for a MOLAP server.

Explain about OLAP and OLTP with suitable examples.

Explain the Data Cubes Computation process in detail.

Write short notes on OLAP Server, Web Usage Mining, Text Mining, and Data Prediction.

Define data cleaning and discuss data cleaning techniques in brief.

Explain the advantages and disadvantages (pros and cons) of data mining.

Describe data transformation methods.

List and explain the different types of data on which data mining is applied.

Explain data mining task primitives in detail.

Discuss the role of the data mining engine in the KDD process.

What is Association Rule Mining? Discuss about the Boolean Association Rule Mining.

Explain techniques to improve the efficiency of the Apriori algorithm.

Write short notes on any two:

Explain the Apriori algorithm to find frequent itemsets with a suitable example.

Explain the FP-growth algorithm with a suitable example.

Differentiate between top-down and bottom-up hierarchical clustering algorithms.

Define clustering and discuss important clustering methods in data mining.

Explain the learning phase in classification and discuss the Decision Tree Induction algorithm in detail.

Define Classifier. Explain the process of verifying the accuracy of classifier.

Explain rule-based algorithms for data classification.

Define probabilistic classifiers and explain them.