Data Warehousing and Data Mining

• Chapter 1: Introduction to Data Warehousing. Notes and past questions on Introduction to Data Warehousing for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 2: Introduction to Data Mining. Notes and past questions on Introduction to Data Mining for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 3: Data Preprocessing. Notes and past questions on Data Preprocessing for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 4: Data Cube Technology. Notes and past questions on Data Cube Technology for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 5: Mining Frequent Patterns. Notes and past questions on Mining Frequent Patterns for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 6: Classification and Prediction. Notes and past questions on Classification and Prediction for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 7: Cluster Analysis. Notes and past questions on Cluster Analysis for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 8: Graph Mining and Social Network Analysis. Notes and past questions on Graph Mining and Social Network Analysis for TU BSc CSIT Data Warehousing and Data Mining.
• Chapter 9: Mining Spatial, Multimedia, Text and Web Data. Notes and past questions on Mining Spatial, Multimedia, Text and Web Data for TU BSc CSIT Data Warehousing and Data Mining.

• When do we prefer trim mean for statistical description of data? Justify with an example. Describe about multi-dimensional data model and... (2081)
• How do you generate strong association rules? From the following dataset find the frequent item set using FP growth algorithm using 3 as... (2081)
• Decision Tree Classification with ID3 Algorithm (2081)
• Using k-means++ algorithm and Euclidean distance, find the initial 3 cluster centroids from A1 = (3, 11), A2 = (3, 6), A3 = (9, 5), A4 =... (2081)
• Explain the general strategies for cube computation. [5] (2081)
• Distinguish between data characterization and data discrimination. What are the challenges of multimedia mining? [5] (2081)
• Define graph mining. Discuss the conflict between theory of balance and theory of status. [5] (2081)
• What is support vector? How do you evaluate the accuracy of a classifier? Describe. [5] (2081)
• Differentiate between k-means and k-medoids clustering algorithm. [5] (2081)
• List any two OLAP operations with example. How do you compute rule coverage and rule accuracy? [5] (2081)
• Define link mining. What are the roles of epsilon and MinPts in DBSCAN. [5] (2081)
• Describe any two methods of handling noisy data. [5] (2081)
• Apriori Algorithm - Frequent Itemsets and Association Rules (2080)
• How Classification Differs from Regression. Train ID3 Classifier (2080)
• Why the concept of data mart is important? Discuss different data warehouse schema with examples.[10] (2080)

Frequently asked questions

Based on the official syllabus.