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This particular Assignment references the syllabus chosen for the subject of Computer Application, for the July 2023 - January 2024 session. The code for the assignment is MCS-221 and it is often used by students who are enrolled in the MCA (Revised) Degree.
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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.
Q1. a) Compare and contrast operational database systems with a Data Warehouse.
b) Define a Data Mart. What is the importance of Data Marts? Compare and contrast Data Mart with Data Warehouse.
Q2. a) Along with the details, draw and illustrate Star, Snowflake and Fact Constellation schemas.
Note: Give examples other than the ones discussed in the course material.
b) Discuss the three-tier data warehouse architecture with a neat diagram.
Q3. Discuss any two Use Cases of implementing Data Warehouse in organizations which includes their dimensional design, ETL, data quality, security aspects, dash boards, data mining techniques etc.
Q4. a) Compare k-Means with k-Medoids algorithms for clustering with the help of suitable examples.
b) How to evaluate clustering algorithms? Explain with suitable examples.
Q5. a) What are key issues in hierarchical clustering? Explain them.
b) Discuss Agglomerative Hierarchical clustering algorithm with an example.
Q6. a) Explain discretization and concept hierarchy generation for numerical data.
b) Why naive Bayesian classification is called “Naive”? Briefly outline the major ideas of naive Bayesian classification. Explain Naive-Bayes classification. Give an example to illustrate your answer.
Q7. Describe the functionalities of Orange Data Mining tool. Discuss how it is used for developing, testing, and visualizing data mining workflows. Also illustrate a Use Case of it.
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