Tribhuvan University
Bachelor of Science in Computer Science and Information Technology
Semester 7 · TU Board 2080
Course Title: Data Warehousing and Data Mining (CSC410)
Full Marks: 60Pass Marks: 24Time: 3 hours
Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate full marks.
Group A
Attempt any TWO questions.(2 × 10 = 20)
- 1.10
State Apriori property. Find frequent item sets and association rules from the transaction database given below using Apriori algorithm. Assume min. support is 50% and min confidence is 75%.
Transaction ID
Items Purchased
1
Bread, Cheese, Egg, Juice
2
Bread, Cheese, Juice
3
Bread, Milk, Yogurt
4
Bread, Juice, Milk
5
Cheese, Juice, MilkAnswer comingAlso asked in 2079
- 2.10
How classification differs from regression. Train ID3 classifierusing the dataset given below. Then predict class label for the data [Age=Mid, Competition=Yes, Type=HW].
[figure in the original paper]
- 3.10
Why the concept of data mart is important? Discuss different data warehouse schema with examples.
Group B
Attempt any EIGHT questions.(8 × 5 = 40)
- 4.5
How KDD differs from data mining? Explain various stages of KDD with suitable block diagram.
- 5.5
Discuss different ways of smoothing noisy data along with suitable examples.
- 6.5
How many cuboids are possible from 5-dimensional data? Discuss the concept of full cube and iceberg cube.
- 7.5
How K-medoids clustering differs from K-means clustering? Divide the following data points into two clusters using kmedoids algorithm. Show computation up to 3 iterations. {(70,85), (65,80), (72,88), (75,90), (60,50), (64,55), (62,52), (63,58)}.
- 8.5
Discuss working of DBSCAN algorithm.
- 9.5
Which algorithm is used for training multi-layer perceptron? Discuss the algorithm in detail.
- 10.5
Explain the OLAP operations with examples.
Answer comingAlso asked in Model question
- 11.5
Discuss the concept of multimedia data mining along with the concept of similarity search.
- 12.5
Write down short notes on:
- Support Vector Machine
- Multi-dimensional Data Model
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