Data Warehousing and Data Mining

• Chapter 1: Data Warehouse Fundamentals. Notes and past questions on Data Warehouse Fundamentals for TU BIT Data Warehousing and Data Mining.
• Chapter 2: Data Preprocessing and Preparation. Notes and past questions on Data Preprocessing and Preparation for TU BIT Data Warehousing and Data Mining.
• Chapter 3: Data Mining Fundamentals and Goals. Notes and past questions on Data Mining Fundamentals and Goals for TU BIT Data Warehousing and Data Mining.
• Chapter 4: Classification Techniques. Notes and past questions on Classification Techniques for TU BIT Data Warehousing and Data Mining.
• Chapter 5: Clustering Methods. Notes and past questions on Clustering Methods for TU BIT Data Warehousing and Data Mining.
• Chapter 6: Association Rule Mining. Notes and past questions on Association Rule Mining for TU BIT Data Warehousing and Data Mining.
• Chapter 7: Advanced Mining Techniques. Notes and past questions on Advanced Mining Techniques for TU BIT Data Warehousing and Data Mining.
• Chapter 8: Social Network Analysis. Notes and past questions on Social Network Analysis for TU BIT Data Warehousing and Data Mining.

• List different clustering approaches. Write the algorithm of K-means algorithm. What are its limitations?[10] (2081)
• What do you understand by classification by back propagation? How is it different from Bayesian classification? Explain.[10] (2081)
• Generate the frequent itemset from the following data using the Apriori algorithm and find the strong association rules. Minimum Support... (2081)
• What do you mean by text mining? Explain with an example. [5] (2081)
• How do you carry out social network analysis? List two of its uses. [5] (2081)
• Illustrate the use of outlier analysis with an example. [5] (2081)
• Why is data preprocessing required? Explain any two data preprocessing methods. [5] (2081)
• Differentiate between data warehouse and operational database. [5] (2081)
• What are the data mining functionalities? Explain. [5] (2081)
• Write short notes on: (a) OLAP (b) Laplace Smoothing [5] (2081)
• Define full cube. Discuss the working mechanism of beam search. [5] (2081)
• Explain any two conceptual modeling techniques of data warehousing. [5] (2081)
• List some of the data mining goals.Explain the components of data warehouse.[3+7] (2082.1)
• What is the concept behind market basket analysis?How do you generate frequent item sets using Apriori algorithm? Explain.[4+6] (2082.1)
• How can you use Gini index as attribute selection algorithm? Illustrate with an example.Describe the working mechanism of support vector... (2082.1)

Frequently asked questions

Based on the official syllabus.