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

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

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

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

Explain the importance of data marts in a data warehouse.

Compare and contrast operational database systems with data warehouse systems.

Explain whether a data warehouse is a prerequisite for data mining and in what ways it helps data mining.

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.

Compare and contrast online transaction processing with online analytical processing.

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

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

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

Explain different data types used in clustering.

Explain the various data mining tasks with appropriate examples.

Discuss about any two measures of similarity.

Discuss about issues in data mining.

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

Explain the general approach to solving a classification problem with a neat block diagram.

Define classification, discuss clustering as supervised learning, and explain how classifier accuracy is determined and its various types.

Discuss about K-nearest-neighbors algorithm.

Why is Naive Bayesian classification called "Naive"? Briefly outline the major ideas of Naive Bayesian classification. Explain Naive-Bayes classification.

Explain Decision tree induction algorithm for classification. Discuss the usage of information gain in this.

Discuss about neural network based Algorithms.

How to obtain useful / interesting association rules from a large set of rules mined from a transactional database?

Explain the BIRCH scalable Algorithm.

Give an example dataset where the Apriori pruning check increases cost.

What are key issues in hierarchical clustering? Explain.

Illustrate FP-growth algorithm with a suitable example.

Write short notes on any two of the following.

Write short notes on any two :

Write short notes on any two: