BSc CSIT (TU) Science Data Warehousing and Data Mining (BSc CSIT, CSC410) Question Paper 2081
This is the official BSc CSIT (TU) (Science stream) Data Warehousing and Data Mining (BSc CSIT, CSC410) question paper for 2081, as set in the regular annual examination. It carries 60 full marks and a time allowance of 180 minutes, across 12 questions. On Kekkei you can attempt this Data Warehousing and Data Mining (BSc CSIT, CSC410) past paper online with a timer, get instant AI feedback and step-by-step solutions, and track the topics where you lose marks — completely free. Whether you are revising for your BSc CSIT (TU) Data Warehousing and Data Mining (BSc CSIT, CSC410) exam or solving previous years' question papers, this 2081 paper is a great way to practise under real exam conditions.
Section A: Long Answer Questions
Attempt any TWO questions.
What is data mining? Explain the knowledge discovery in databases (KDD) process with a diagram, and discuss major data mining functionalities.
Explain association rule mining. Apply the Apriori algorithm on a sample transaction dataset to generate strong association rules using given minimum support and confidence.
Explain classification using a decision tree. Construct a decision tree using ID3 for the given dataset and predict the class label of a new instance.
Section B: Short Answer Questions
Attempt any EIGHT questions.
Define data warehouse and list its key features.
Compare MOLAP, ROLAP, and HOLAP.
Explain the slice and dice OLAP operations.
What are the limitations of the Apriori algorithm?
Explain the DBSCAN algorithm and the concepts of core, border, and noise points.
What is normalization in data preprocessing? Explain min-max normalization.
Differentiate between classification and prediction.
Explain the K-medoids clustering method.
Write short notes on the applications of data mining.