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Wednesday, 5 April 2017

IPU BCA Semester 6 - Data Warehouse and Data Mining - End Term Paper 2016

END TERM EXAMINATION

SIXTH SEMESTER |BCA| MAY- JUNE 2016

Paper Code: BCA-302

Subject: Data Warehouse and Data Mining

IPU BCA Semester 6 - Data Warehouse and Data Mining - End Term Paper 2016

Time: 3 Hours Maximum Marks: 75

Note: Attempt any six questions including Question 1 which is compulsory.


Q1: (2.5 × 10 = 25)
(a) Explain in brief how the evolution of database technology led to data mining?

(b) Write the names of steps involved in data mining when viewed as a process of knowledge discovery?

(c) Mention the name of databases and information repositories on which data mining can be performed.

(d) How does classification work in data mining? How is (numeric) prediction different from classification?

(e) Mention the criteria for the comparison and evaluation of classification methods during data mining.

(f) Suppose that the data for analysis includes the attribute age. The 'age' values for the data tuples are (in increasing order):
13,21, 22, 25, 25, 30, 30, 33, 35, 35, 35, 36, 40, 52, 70
Estimate the mean of data? Find the first quartile (Q1) and the third quartile (Q3) of the data? Give five number summary of data.

(g) What is the use of summary tables in data warehouse?

(h) What can we do to secure the privacy of individuals while collecting and mining data?

(i) Suppose a group of 12 sales price records has been sorted as follows:
5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215
Partition them into three bins by each of the following methods:
i. equal frequency (equal-depth) partitioning or equal-width partitioning
ii. clustering




Q2:
Imaging that you need to analyze 'All Electronics' sales and customer data (Data related to the sales of electronic items). You note that many tuples have no recorded value for several attributes such as customer income. How can you about filling in the missing values for this attribute? Explain some of the methods to handle the problem. (10)

Q3:
Describe the major issues in data mining regarding mining methodology, user interaction, performance and diverse data types in detail. (10)

Q4:
Define Data warehouse. What are the features which distinguish data warehouses from other data repository systems such as relational database systems, transaction processing systems and file systems?

Q5:
How do data warehousing and OLAP relate to data mining? Briefly compare between OLTP and OLAP systems from the following perspective:
(i) Users and System Orientation
(ii) Data content
(iii) Database design.
Draw a figure for Star schema and Snowflake schema of a data warehouse. Consider any data warehouse of your choice for sales records. (10)

Q6:
Consider a database that has five transactions. Let min sup = 60% and min con f = 80%
TID Items Bought
T100 {M,O,N,K,E,Y}
T200 {D,O,N,K,E,Y}
T300 {M,A,K,E}
T400 {M,U,C,K,Y}
T500 {C,O,O,K,I,E}

Find all frequent item-sets using Apriori and FP-growth respectively. Compare the efficiency of the two mining processes. (10)

Q7: Describe the major issues during pre-processing the data for classification and prediction. (10)

Q8: Write the name of the types of data that often occur in cluster analysis and how to preprocess them for such an analysis? (10)

Q9: Discuss the applications of data mining in the following: (10)
(a) Retail industry
(b) Telecommunication Industry
(c) Biological Data Analysis

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