DMBI

 

Data Mining & Business Intelligence

2170715

Important Question Bank for GTU Exam

1.    Compare OLTP & OLAP systems.

2.     Do feature wise comparison between BI and DW

3.       What is Data Mining? Why is it called data mininig rather knowledge mininig? Explain KDD process

4.       Explain Star, Snowflake, and Fact Constellation Schema for Multidimensional Database.

5.    Explain Mean, Median, Mode, Variance, Standard Deviation & five number summary with suitable database example.

6.       Define data cube and explain 3 operations on it.

7.       Use min-max normalization method to normalize the following group of data by setting min = 0 and max = 1 200, 300, 400, 600, 1000

8.       Explain the following data normalization techniques: (i) min-max normalization and (ii) decimal scaling.

9.       Differentiate Fact table vs. Dimension table

10.   Explain the following as attribute selection measure:

                                                               i.      Information Gain

                                                             ii.      Gain Ratio

11.   Compare association and classification. Briefly explain associative classification with suitable example

12.   Do feature wise comparison between classification and prediction

13.   Define following terms:

a.       Data Mart

b.      Enterprise Warehouse

c.       Virtual Warehouse

14.   Generate frequent item sets and generate association rules based on it using apriori algorithm. Minimum support is 50% and minimum confidence is 70%

15.   TID	16.   Items
17.   100	18.   1,3,4
19.   200	20.   2,3,5
21.   300	22.   1,2,3,5
23.   400	24.   2,5

 

25.   Define Big Data. Discuss various applications of Big Data.

26.   Explain cluster analysis and outlier analysis with example

27.   What is noise? Explain binning methods for data smoothing.

28.   Describe Concept Hierarchy? List and briefly explain types of Concept Hierarchy

29.   Briefly outline the major steps of decision tree classification. Why tree pruning useful in decision tree induction?

30.   Define “clustering”? Mention any two applications of clustering

31.   Briefly explain Linear and Non-linear regression

32.   Describe Concept Hierarchy? List and briefly explain types of Concept Hierarchy

33.   What is Big Data? What is big data analytic ? Explain the big data- distributed file system

34.   Explain data mining application for fraud detection

35.   Discuss the main features of Hadoop Distributed File System

36.   What is market basket analysis? Explain the two measures of rule interestingness: support and confidence

37.   Explain why data warehouses are needed for developing business solutions from today’s perspective. Discuss the role of data marts

38.   Explain Spatial mining using example

39.   Calculate the weights using neural network single layer perceptron model. Three inputs are x0, x1, x2, bias and weights are as follows: w1(0) = 30 , w2(0) = 300 b(0)= 50 , η=0.01, xo = +1 Activation function is : sgn(x) = +1, if x>=0 sgn(x) = -1, if x<0

a.       Calculate x2 for x1=100 and & 200.

b.      For bias b(0)= -1230 recalculate the weights w1 and w2.

40.   How data Mining is useful for Business Intelligence applications viz. Balanced Scorecard, Fraud Detection, Click stream Mining, Market Segmentation, retail industry, telecommunications industry, banking & finance and CRM

41.   Explain text mining using example

42.   Generate decision tree using CART algorithm for the following dataset.

Sr. No	Outlook	Temperature	Humidity	Wind	Play
1	Sunny	Hot	High	FALSE	No
2	Sunny	Hot	High	TRUE	No
3	Overcast	Hot	High	False	Yes
4	Rain	Mild	High	FALSE	Yes
5	Rain	Cool	Normal	FALSE	Yes
6	Rain	Cool	Normal	True	No
7	Overcast	Cool	Normal	True	Yes
8	Sunny	Cool	Normal	False	Yes
9	Sunny	Mild	High	False	No
10	Rain	Mild	Normal	False	Yes
11	Sunny	Mild	Normal	True	Yes
12	Overcast	Mild	High	True	Yes
13	Overcast	Hot	Normal	False	Yes
14	Rain	Mild	High	True	No