Data Mining And Business Intelligence (2170715)

BE | Semester 7 
  Winter - 2017|18/11/2017
Total Marks 70

Q1 (a) Explain measures for finding rule interestingness (support, confidence) with example

Q1 (b) Differentiate between OLTP and OLAP

Q1 (c) What is Data Mining? Write down short note on KDD process

Q2 (a) Use min-max normalization method to normalize the following group of data by setting min = 0 and max = 1 200, 300, 400, 600, 1000 03

Q2 (b) Describe various methods for handling missing data values

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}

OR

Q2 (c) Compare association and classification. Briefly explain associative classification with suitable example

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 equal-frequency (equi-depth) partitioning method. Perform data smoothing by bin mean.

Q3 (b) Define Big Data. Discuss various applications of Big Data

Q3 (c) What are the major issues in Data Mining?

OR

Q3 (a) Explain Prepruning and Postpruning with an example

Q3 (b) Define the following terms: Business Intelligence, Data Mart, Closed frequent itemset, Outlier Analysis

Q3 (c) Why naïve Bayesian classification is called “naïve”? Describe naïve Bayesian classification with example

Q4 (a) What is classification and prediction? List out Issues regarding Classification and prediction

Q4 (b) Explain Star schema and Snowflake schema with example.

Q4 (c) Explain Hadoop storage – HDFS.

OR

Q4 (a) Explain the following terms: Numerosity reduction, Data Integration, Data transformation

Q4 (b) Explain data mining application for fraud detection

Q4 (c) Explain sampling methods for data reduction

Q5 (a) Explain various OLAP operations

Q5 (b) Explain basic concepts of text mining and web mining

OR

Q5 (a) What is an attribute selection measure? Explain different attribute selection measures with example.

Q5 (b) Explain Data warehouse architecture