Assignment III DMDW 2015-16 BEIT

MGM’s College of Engineering, Nanded.
Department of IT
Semester I (2015-16)
Class: BE(IT)       Subject: DMDW         Assignment III
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1.  For the following transaction database, find out the frequent itemsets with support  ≥ 50 %.

Tid	Items
T1	N, P, O, M
T2	E,P,O
T3	N, B, M, F
T4	N, V, D, Z
T5	D,P,P,M
T6	P, O

2. Discuss the essential steps of apriori algorithm.

3. For the above transaction database, find the itemsets having confidence ≥ 60 %.

4. What are the differences between Classification, Clustering & Prediction ?

5. What are the various forms of presenting and visualizing the discovered patterns?

6. What is information gain? How Information Gain is calculated?

7. For the following dataset, calculate Information Gain for attribute age.

 

 

8. Discuss the Multilevel Association Rules mining for transaction database.

9. State and explain the Bayesian algorithm.

10. Create a Naïve Bayesian Classifier for the following dataset. Classify the sample      X={ rain, hot, high, false }

                                               

                      

      

11.Explain the decision tree induction algorithm.

12. What are the different techniques to calculate the distance between patterns?

13. Explain the different types of cluster analysis methods and discuss their features.

14. Describe k-means algorithm and discuss its strengths and weaknesses.

15. Discuss the taxonomy of web mining.
16. The following table shows a set of paired data where x is the number of years of work experience of a college graduate and y is corresponding salary of the graduate. There is a linear relationship between the two variables, x and y. Use Straight-line regression method with least squares and predict the salary of a college graduate with 10 years of experience.

x years experience	y salary (in $1000s)
5	32
10	59
11	66
15	74
5	38
8	45
13	61
23	92
3	22
18	85

      

Faculty Incharge: Hashmi S A