B.TechSemester 82023-24Data Warehousing Data MiningKOE093

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

B.Tech · Semester 8 · Free PDF Download

This is the official AKTU Data Warehousing Data Mining Previous Year Question Paper for B.Tech Semester 8, academic session 2023-24. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech

Semester:Semester 8

Session:2023-24

University:AKTU / UPTU

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Questions Asked in 2023-24

Data Warehousing Data Mining (KOE093) — extracted question text · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20

a
Define Data Warehousing. 2 1
b
Discuss the Fact Constellation. 2 1
c
Explain Distributed DBM S implementation. 2 2
d
Define Warehousing Software. 2 2
e
Are all the patterns interesting? 2 3
f
Differentiate between binary sy mmetric attributes and asymmetr ic attributes
g
Find the mode of the following dataset: 12,13,34,32,21,29,40,1 1,39,23. What is the advantage of mode over mean and median?
h
Given two objects represented by the tuples (22, 2, 45, 10) an d (20, 10, Compute the Manhattan distance between these two ob jects. i. What do you mean by Temporal Mining? 2 5 j. Discuss Data Visualization. 2 5

Section BAttempt any three o f t h e f o l l o w i n g : 10 x 3= 30

a
Write short notes on: i. Steps of Knowledge Discovery in data ii. Explain Snow Flakes in detail
b
Explain Market Basket Analysis. 10 2
c
Draw the box-and-whisker plot of the following dataset: 4.3, 5.1, 3.9
d
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 dist ance algorithm only)
e
Explain ROLAP, MOLAP and HOLAP in detail. 10 5

Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10 x 1= 10

a
How mapping a 2D table into multidimensional data model? Exp lain with suitable example
b
Write short notes on: i. Data Characterization and Data Discrimination ii.Snow Flakes in detail
a
Differentiate between: (i) Min-Max and Z-score Normalization with examples (ii) Binary data variables and Nominal data variables with examples
b
Explain the major components of Data Mining Architecture. 1 0 2
a
Discuss Decision tree-base d classifiers in detail. 10 3
b
Classify the tuple X = (age = youth, income = medium, studen t= yes, credit rating = fair) using Bayes Theorem. RID age income student credit_rating Class: 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
a
Explain various types of clustering methods. Discuss any one partitioning clustering algorithm
b
Discuss DBSCAN clustering algor ithm with suitable example. 10 4
a
Differentiate between
a
OLAP and OLTP in detail
b
Slice and Dice operations with an example
b
Define Spatial Data? How min ing of spatial data is done? 10 5

Question text is extracted from the official AKTU question paper PDF above. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.

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