IGNOU MCS-221
- Data Warehousing and Data Mining,
Latest Solved Assignment
(July 2024 - January 2025)

Q1: Discuss the role of ETL (Extract, Transform, Load) processes in data warehousing. Provide a detailed explanation of each phase and its importance. Illustrate your answer with examples of common tools used in ETL and the challenges that may arise during these processes.

Q2: (a) Explain the concept of Data Warehousing architecture. Compare and contrast the different types of architectures such as Single-tier, Two-tier, and Three-tier. Provide examples of scenarios where each architecture might be most beneficial.

(b) Analyze the concept of OLAP (Online Analytical Processing) and its significance in data warehousing. Describe the differences between MOLAP, ROLAP, and HOLAP. Discuss the advantages and disadvantages of each type with respect to data analysis and querying performance.

Q3: Design a data warehouse schema for a retail company. Include fact tables, dimension tables, and consider the star schema and snowflake schema designs. Justify your design choices and discuss how your schema supports efficient query processing and business intelligence needs.

Q4: Explain the use of metadata in data warehousing. Discuss the different types of metadata and their roles. Provide examples of how metadata can enhance the usability, maintenance, and performance of a data warehouse.

Q5: Evaluate the role of data warehousing in supporting business intelligence and analytics. Discuss the process of transforming raw data into actionable insights. Provide examples of business intelligence tools and techniques that leverage data warehousing to enhance decision-making processes.

Q6: Analyze various data pre-processing techniques such as data cleaning, data integration, data transformation, and data reduction. Explain the significance of each technique in improving the quality of data for mining and provide examples of scenarios where each technique would be applied.

Q7: Compare and contrast the various classification algorithms used in data mining, such as Decision Trees, Naive Bayes, Support Vector Machines, and Neural Networks. Discuss the strengths and weaknesses of each algorithm and provide examples of appropriate use cases for each.

Q8: Evaluate the different clustering techniques, including K-means, hierarchical clustering and DBSCAN. Explain the underlying principles of each technique, and discuss their advantages, limitations, and practical applications.

Q9: Examine the role of association rule mining in data mining. Describe the Apriori algorithm and its variations. Discuss the challenges associated with association rule mining, such as the generation of large numbers of rules and the need for efficient computation.

Q10: Analyze the role of feature selection and dimensionality reduction in data mining. Discuss techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and feature selection algorithms. Explain how these techniques help in improving model performance and reducing computational complexity.

Q1: a) Describe the measures of Multidimensional data modelling. Give examples.
b) “Data preprocessing is necessary before Data Mining process”. Justify your answer.

Q2: a) Give 2 examples for Star schema, Snowflake schema and Fact Constellation Schema.
Note: Give examples other than the ones discussed in the course material.
b) Explain Multilevel Association Rules with appropriate examples.

Q3: Discuss any two Use Cases of implementing Data Marts in organizations which includes their dimensional design, ETL, data quality, security aspects, dash boards, data mining techniques etc.

Q4: a) What is/are the main objective(s) of Classification? Give the categorization of Classification Approaches. Also, explain how to evaluate the Clustering models?
b) Write and explain the basic Agglomerative Hierarchical Clustering Algorithm.

Q5: a) What are the advantages of PAM method? Explain them.
b) Explain how to cluster the data sets using k-mediod clustering alogirhtm?

Q6: a) Compare k-Means Vs k-Medoids algorithms for Clustering.
b) What is the main objective of Clustering? Give the categorization of Clustering Approaches. Also, explain how to evaluate the Clustering Algorithms?

Q7: Describe the functionalities of Rattle Data Mining tool. State whether it is a proprietary or open source tool? Discuss the process of installation. Describe its complete set of features? Explain its comprehensive and well-developed user interface? With the help of integrated log code tab, explain how it produces the duplicate code for GUI operations? Briefly, explain the support of visualization elements in the tool. Also illustrate a UseCase of it.