AKTU Data Warehousing & Data Mining (KOE093) Question Paper 2023-24

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

1. Define Data Warehousing.
2. Discuss the Fact Constellation.
3. Explain Distributed DBMS implementation.
4. Define Warehousing Software.
5. Are all the patterns interesting?
6. Differentiate between binary symmetric attributes and asymmetric attributes.
7. Find the mode of the following dataset: 12,13,34,32,21,29,40,11,39,23. What is the advantage of mode over mean and median?
8. Given two objects represented by the tuples (22, 2, 45, 10) and (20, 10, 2 4 26, 2): Compute the Manhattan distance between these two objects.
9. What do you mean by Temporal Mining?
10. Discuss Data Visualization.

Section B

Q2. Attempt any three of the following · 10 × 3 = 30 marks

1. Write short notes on: i. Steps of Knowledge Discovery in data ii. Explain Snow Flakes in detail.
2. Explain Market Basket Analysis.
3. Draw the box-and-whisker plot of the following dataset: 4.3, 5.1, 3.9, 10 3 4.5, 4.4, 4.9,5.0, 4.7, 4.1, 4.6, 4.4, 4.3, 4.8, 4.4, 4.2, 4.5, 4.4.
4. Cluster the following dataset with points (2,4), (6,8), (1,2), (4,5), (3,5) into two clusters using K-Means algorithm (using Euclidean distance algorithm only).
5. Explain ROLAP, MOLAP and HOLAP in detail.

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. How mapping a 2D table into multidimensional data model? Explain with suitable example.
2. Write short notes on: i. Data Characterization and Data Discrimination ii.Snow Flakes in detail.

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. Differentiate between: (i) Min-Max and Z-score Normalization with examples (ii) Binary data variables and Nominal data variables with examples
2. Explain the major components of Data Mining Architecture.

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. Discuss Decision tree-based classifiers in detail.
2. Classify the tuple X = (age = youth, income = medium, student= yes, 10 3 credit rating = fair) using Bayes Theorem.
   Class:
   RID age income student credit_rating buys_computer
   1 youth high no fair no
2 youth high no excellent no
   3 middle_aged high no fair yes
   4 senior medium no fair yes
   5 senior low yes fair yes
   6 senior low yes excellent no

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain various types of clustering methods. Discuss any one partitioning clustering algorithm.
2. Discuss DBSCAN clustering algorithm with suitable example.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. Differentiate between (a) OLAP and OLTP in detail. (b) Slice and Dice operations with an example.
2. Define Spatial Data? How mining of spatial data is done?

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