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
Winter2017
BE  Semester
7
Winter  2017

18/11/2017
Total Marks
70
Q1
(a)
Explain measures for finding rule interestingness (support, confidence) with example
3 Marks
Q1
(b)
Differentiate between OLTP and OLAP
4 Marks
Q1
(c)
What is Data Mining? Write down short note on KDD process
7 Marks
Q2
(a)
Use minmax normalization method to normalize the following group of data by setting min = 0 and max = 1 200, 300, 400, 600, 1000 03
3 Marks
Q2
(b)
Describe various methods for handling missing data values
4 Marks
Q2
(c)
Using Apriori algorithm, generate frequent item sets (min_sup >= 33.3%) for the following transaction database.
TransID
Item List
T1
{A,B,D,K}
T2
{A,B,C,D,E}
T3
{A,B,C,E}
T4
{B,D}
T5
{A,C}
T6
{B,D}
7 Marks
OR
Q2
(c)
Compare association and classification. Briefly explain associative classification with suitable example
7 Marks
Q3
(a)
Suppose a group of sales price records has been sorted as follows: 6, 9, 12, 13, 15, 25, 50, 70, 72, 92, 204, 232 Partition them into three bins by equalfrequency (equidepth) partitioning method. Perform data smoothing by bin mean.
3 Marks
Q3
(b)
Define Big Data. Discuss various applications of Big Data
4 Marks
Q3
(c)
What are the major issues in Data Mining?
7 Marks
OR
Q3
(a)
Explain Prepruning and Postpruning with an example
3 Marks
Q3
(b)
Define the following terms: Business Intelligence, Data Mart, Closed frequent itemset, Outlier Analysis
4 Marks
Q3
(c)
Why naïve Bayesian classification is called “naïve”? Describe naïve Bayesian classification with example
7 Marks
Q4
(a)
What is classification and prediction? List out Issues regarding Classification and prediction
3 Marks
Q4
(b)
Explain Star schema and Snowflake schema with example.
4 Marks
Q4
(c)
Explain Hadoop storage – HDFS.
7 Marks
OR
Q4
(a)
Explain the following terms: Numerosity reduction, Data Integration, Data transformation
3 Marks
Q4
(b)
Explain data mining application for fraud detection
4 Marks
Q4
(c)
Explain sampling methods for data reduction
7 Marks
Q5
(a)
Explain various OLAP operations
7 Marks
Q5
(b)
Explain basic concepts of text mining and web mining
7 Marks
OR
Q5
(a)
What is an attribute selection measure? Explain different attribute selection measures with example.
7 Marks
Q5
(b)
Explain Data warehouse architecture
7 Marks