Data Mining and Warehousing (AD-603 (A)) - Important Questions

Explain the three-tier data warehouse architecture with a neat diagram.

Explain the steps involved in data preprocessing, including dimensionality reduction techniques.

Discuss metadata in the context of data warehousing and its role in data management, including support for data retrieval, transformation, and analysis.

Explain typical OLAP operations on multidimensional data cubes with examples and diagrams.

Discuss the hardware and operational design considerations for a data warehouse to support OLAP queries and data mining operations.

Differentiate ROLAP, MOLAP and HOLAP server functionalities.

Explain the various phases of the knowledge discovery (KDD) process and data mining as a step within it, with a neat diagram.

Explain why data preprocessing is necessary before data mining and describe the data preprocessing tasks.

Explain the concept of fuzzy sets and fuzzy logic and how fuzzy logic can be applied to data mining.

Explain the rule-based classifier/algorithm including structure, working, example, use of information gain, real-world applications, strengths, limitations and future evolution.

With a neat block diagram, explain the general approach to solve a classification problem.

Discuss about K-nearest-neighbors (KNN) algorithm with example.

For a transactional database, we can get a large number of association rules. How can we get useful/interesting association rules from such a large set?

Explain the BIRCH scalable algorithm with example.