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

Explain the different types of data warehouse / multidimensional schemas (star schema, snowflake schema, 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.

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

Describe the techniques used for data preprocessing in data warehousing.

Describe in brief about data warehouse implementation.

Discuss in detail the case study of data warehouse for the Government of Madhya Pradesh.

Define data cleaning and explain different techniques for handling missing values.

Explain the activities/methods of data cleaning and the associated process in detail.

Enumerate the building blocks of a data warehouse and explain the importance of metadata and challenges in metadata management.

Explain how a data mining system can be integrated with a data warehouse, with example.

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

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

Write short notes on a choice of topics including OLAP queries and operations, summary statistics, multidimensional data model, and neural network-based algorithms.

Describe backup and recovery strategies in data warehouse environments.

Explain the role of Hardware components in Operational Design of Data Warehouse.

Explain about how to use OLAP tools on the Internet.

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

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)

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

Explain the differences between classification and clustering with examples and applications.

Describe similarity measures and their role in clustering and classification.

Discuss the key differences between supervised and unsupervised learning with examples.

Explain the data mining functionalities.

Explain briefly about statistical distribution-based outlier detection.

Explain different data types used in clustering.

Explain the various data mining functionalities and issues in detail.

Explain the need for data preprocessing and describe the various preprocessing tasks/forms.

Describe the k-Nearest Neighbors (k-NN) algorithm and its role in classification.

What are rule-based classifiers and how are classification rules derived from data?

Define probabilistic classifiers and explain how they differ why are they useful in uncertain environments?

Explain various learning techniques involved in supervised learning.

Explain distance-based algorithms.

Explain decision tree-based algorithms.

Explain the following clustering methods in detail.

Explain whether association rule mining is supervised or unsupervised learning.

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

Define clustering and explain its importance in unsupervised learning.

Define association rules and differentiate Apriori and FP-Growth algorithms.

What is the goal of clustering? How does partitioning around medoids algorithm achieve this?

Explain the Apriori algorithm for finding frequent itemsets with an example.

Explain about data mining applications.