Data Mining and Warehousing (CS-703 (B)) - Important Questions

Explain the different types of data warehouse / multidimensional schemas - star schema, snowflake schema and fact constellation schema - with suitable examples.

Define data warehousing and explain the three-tier data warehouse architecture with a suitable diagram.

Explain the role of sourcing, acquisition, cleanup and transformation tools in data warehousing.

Define OLAP and explain/compare the different types/categorization of OLAP servers/tools - MOLAP, ROLAP and HOLAP.

Explain OLAP operations in the multidimensional data model with examples, including roll-up, drill-down, slice, dice and pivot.

Describe backup and recovery strategies in data warehouse environments.

Explain the steps of the KDD process with data mining as a step, with diagrammatic illustration.

Define data mining and differentiate/compare data mining vs knowledge discovery in databases (KDD).

Discuss the primitives for specifying a data mining task with examples and diagrammatic illustration.

Explain the major requirements, issues and challenges in data mining.

Explain distance-based algorithms for classification with suitable examples.

Explain whether association rule mining is supervised or unsupervised learning with justification.

Explain the following clustering methods in detail with examples.

Define clustering and explain the types of data in cluster analysis.

Explain metadata in data warehousing in detail, including its types, role and importance.