Data Mining And Business Intelligence (2170715)

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?

 Unit : Introduction To Data Mining

Q1 (b) Explain various features of Data Warehouse?

 Unit : Overview and concepts Data Warehousing and Business Intelligence

Q1 (c) Differentiate between Operational Database System and Data Warehouse

 Unit : Overview and concepts Data Warehousing and Business Intelligence

Q2 (a) What is the difference between KDD and Data Mining?

 Unit : Introduction To Data Mining

Q2 (b) What is Concept Hierarchy? List and briefly explain types of Concept Hierarchy

 Unit : The Architecture of BI And DW

Q2 (c) Explain Mean, Median, Mode, Variance, Standard Deviation & five number summary with suitable database example.

 Unit : Data Pre-processing

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

Q3 (a) Differentiate Fact table vs. Dimension table

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 min-max normalization to transform the value 45 for age onto the range [0:0, 1:0]

 Unit : Data Pre-processing

Q3 (c) Explain mining in following Databases with example. 1. Temporal Databases 2. Sequence Databases 3. Spatial Databases 4. Spatiotemporal Databases

OR

Q3 (a) List and describe methods for handling missing values in data cleaning.

 Unit : Data Pre-processing

Q3 (b) Explain the following as attribute selection measure: (i) Information Gain (ii) Gain Ratio

 Unit : The Architecture of BI And DW

Q3 (c) Explain three tier data warehouse Architecture in details.

Q4 (a) How K-Mean clustering method differs from K-Medoid clustering method?

Q4 (b) Define data cube and explain 3 operations on it.

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

OR

Q4 (a) Define following terms : Data Mart , Enterprise Warehouse & Virtual Warehouse

Q4 (b) Discuss the application of data warehousing and data mining

Q4 (c) What is web log? Explain web structure mining and web usage mining in detail

Q5 (a) Draw the topology of a multilayer, feed-forward Neural Network.

Q5 (b) Explain Linear regression with example

Q5 (c) Explain the major issues in data mining.

OR

Q5 (a) Briefly explain text mining

Q5 (b) What is market basket analysis? Explain the two measures of rule interestingness: support and confidence

Q5 (c) What is Big Data? What is big data analytic? Explain the big data- distributed file system.