Subjects
Advanced Java  2160707
Compiler Design  2170701
Data Mining And Business Intelligence  2170715
Data Structure  2130702
Database Management Systems  2130703
Information And Network Security  2170709
MathematicsI  3110014
Mobile Computing And Wireless Communication  2170710
Operating System  2140702
Software Engineering  2160701
Theory Of Computation  2160704
Semester
Semester  1
Semester  2
Semester  3
Semester  4
Semester  5
Semester  6
Semester  7
Semester  8
Data Mining And Business Intelligence
(2170715)
DMBI2170715
Winter2018
BE  Semester
7
Winter  2018

03/12/2018
Total Marks
70
Q1
(a)
What is Data Mining? Why is it called data mining rather knowledge mining?
3 Marks
Unit : Introduction To Data Mining
Q1
(b)
Explain various features of Data Warehouse?
4 Marks
Unit : Overview and concepts Data Warehousing and Business Intelligence
Q1
(c)
Differentiate between Operational Database System and Data Warehouse
7 Marks
Unit : Overview and concepts Data Warehousing and Business Intelligence
Q2
(a)
What is the difference between KDD and Data Mining?
3 Marks
Unit : Introduction To Data Mining
Q2
(b)
What is Concept Hierarchy? List and briefly explain types of Concept Hierarchy
4 Marks
Unit : The Architecture of BI And DW
Q2
(c)
Explain Mean, Median, Mode, Variance, Standard Deviation & five number summary with suitable database example.
7 Marks
Unit : Data Preprocessing
OR
Q2
(c)
What is noise? Explain data smoothing methods as noise removal technique to divide given data into bins of size 3 by bin partition (equal frequency), by bin means, by bin medians and by bin boundaries. Consider the data: 10, 2, 19, 18, 20, 18, 25, 28, 22
7 Marks
Q3
(a)
Differentiate Fact table vs. Dimension table
3 Marks
Q3
(b)
Suppose that the data for analysis includes the attribute age.
The age values for the data tuples are (in increasing order): 13, 15, 16, 16, 19, 20, 23, 29, 35, 41, 44, 53, 62, 69, 72
Use minmax normalization to transform the value 45 for age onto the range [0:0, 1:0]
4 Marks
Unit : Data Preprocessing
Q3
(c)
Explain mining in following Databases with example. 1. Temporal Databases 2. Sequence Databases 3. Spatial Databases 4. Spatiotemporal Databases
7 Marks
OR
Q3
(a)
List and describe methods for handling missing values in data cleaning.
3 Marks
Unit : Data Preprocessing
Q3
(b)
Explain the following as attribute selection measure: (i) Information Gain (ii) Gain Ratio
4 Marks
Unit : The Architecture of BI And DW
Q3
(c)
Explain three tier data warehouse Architecture in details.
7 Marks
Q4
(a)
How KMean clustering method differs from KMedoid clustering method?
3 Marks
Q4
(b)
Define data cube and explain 3 operations on it.
4 Marks
Q4
(c)
State the Apriori Property. Generate large itemsets and association rules using Apriori algorithm on the following data set with minimum support value and minimum confidence value set as 50% and 75% respectively
TID
Items Purchased
T101
Cheese, Milk, Cookies
T102
Butter, Milk, Bread
T103
Cheese, Milk, Butter, Bread
T104
Butter, Bread
7 Marks
OR
Q4
(a)
Define following terms : Data Mart , Enterprise Warehouse & Virtual Warehouse
3 Marks
Q4
(b)
Discuss the application of data warehousing and data mining
4 Marks
Q4
(c)
What is web log? Explain web structure mining and web usage mining in detail
7 Marks
Q5
(a)
Draw the topology of a multilayer, feedforward Neural Network.
3 Marks
Q5
(b)
Explain Linear regression with example
4 Marks
Q5
(c)
Explain the major issues in data mining.
7 Marks
OR
Q5
(a)
Briefly explain text mining
3 Marks
Q5
(b)
What is market basket analysis? Explain the two measures of rule interestingness: support and confidence
4 Marks
Q5
(c)
What is Big Data? What is big data analytic? Explain the big data distributed file system.
7 Marks