Data Warehousing and Data Mining
This course introduces advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the current technologies.
Tribhuvan University
Institute of Science and Technology
Dwdm Model Set
Bachelor Level / seventh-semester / Science
Computer Science and Information Technology( CSC410 )
Data Warehousing and Data Mining
Full Marks: 60 + 20 + 20
Pass Marks: 24 + 8 + 8
Time: 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.
Explain the different components of data warehouse. How data cube precomputation is performed? Describe.
Write the limitation of Apriori algorithm. Given the objects P1(2,3), P2(4,5), P3(10,40), P4(60,55), P5(70,80), apply K-means algorithm (K = 2) to show the final clusters after 2 iterations. Assume P1 and P3 as initial cluster centroids.
Consider the following training data set.
Group B
Attempt any EIGHT questions.
List any two challenge of multimedia mining. Differentiate between web usage mining and web content mining.
How trust and distrust propagate in social network Explain.
Why data preprocessing is mandatory? Justify.
Describe any five types of OLAP operations.
Given the following data set, find the frequent itemset using Apriori algorithm with minimum
support 3. (5)
T1 {A, B, C, D, E, F}
T2 {B, C, D, E, F, G}
T3 {A, D, E, H}
T4 {A, D, F, I, J}
T5 {B, D, E, K}
Illustrate the hierarchical clustering with an example.
Discuss about overfitting and underfitting. How precision and recall is used to evaluate classifier.
What is the concept mini batch k-means? How DBSCAN works?
How beam search and logic programming is used to mine graph? Explain.