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
_________________________________________________________________

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

Semester I (2015-16) Assignment II Class: BE(IT) Subject: DMDW

MGM’s College of Engineering, Nanded.
Department of IT
Semester I (2015-16)
Class: BE(IT)       Subject: DMDW         Assignment II
___________________________________________________________________________

1

     1.What is data mining? What features are expected from a of DM output.

2   2.Why data preprocessing is needed in DM? Explain the different forms of data preprocessing.

3   3.Consider that you have to analyze EMart_Customer  sales data. You find that many tuples have no   recorded value for several attributes, such as customer total_amount. How can you fill the missing   values for this attribute?

4   4.Given a numerical attribute such as total_amount in the above data set, how can we smooth out the data to remove the noise?

5   5.Suppose that the data for analysis includes the attribute total_marks. Which distributive, algebraic and holistic measures will be used for data analysis?

6   6.(a) Define Min-max normalization. Suppose the minimum and maximum values for the attribute total_sales are Rs.40000 and Rs.85000, respectively. Using min-max normalization, transform and map value Rs. 76000 to the range [0.0, 1.0].

(b) Define z-score normalization. The mean and standard deviation of the values for the attribute total_marks are 4400 and 890, respectively. Using z-score normalization to transform value 5500.

7   7.Suppose that the data for analysis includes the attribute marks. The marks values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33,33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70.

    (a) What is the mean of the data? What is the median?

    (b) What is the mode of the data? Comment on the data’s modality (i.e., bimodal, trimodal, etc.).

8.For the data set of Q.8 abov

   (c) What is the midrange of the data?

   (d) Can you find (roughly) the first quartile (Q1) and the third quartile (Q3) of the
           data?

9. For the data set of Q.8 above

   (e) Give the five-number summary of the data.

   (f) Show a boxplot of the data.

   (g) How is a quantile-quantile plot different from a quantile plot?

10.  Define the following DM functionalities:characterization,discrimination,association and correlation analysis.

11.  Explain five primitives of data mining with an appropriate example.

12.  Describe the differences between the following approaches for the integration of a 

data mining  system with a database or DW system: no coupling, loose coupling,semitight coupling, and tight coupling.13.What is KDD? Explain the stages of KDD with an appropriate example.

14.Use the two methods below to normalize the following group of data: 2000,3000,4000,6000,10000

       (a) min-max normalization by setting min=0 and max=1

       (b) z-score normalization

15. Define (i) Association rule mining (ii) Support  (iii) Confidence.

16. Enlist applications of Data Mining and Association rule mining.

Faculty Incharge: Hashmi S A

SEM I 2015-16 Subject:DMDW Class:BE(IT) Assignment I

MGM’s College of Engineering, Nanded.
Department of IT
Semester I (2015-16)
Class: BE(IT)       Subject: DMDW         Assignment I

1. Define data warehouse. Explain the characteristics of DW.
2. Compare DBMs with DW.
3. Draw and explain star schema with an example.
4. Enlist the features of snowflake and fact constellation schema with appropriate
    example.
5. What are the major components of data warehouse architecture? Explain with
    block diagram.
6. Describe the OLAP operations with an appropriate example.
7. Draw and explain ROLAP, MOLAP and HOLAP models of DW.
8. Suppose that a data warehouse consists of the three dimensions Time,
    Trainer, and Trainee, and the two measures count and Fee, where fee is  
     charged by the trainer for training.
    Draw a star and snowflake schema diagram for the above data warehouse.
9. Why metadata is important for DW?
10. What back-end tools and utilities are used in DW? Why?
11. Enlist and explain the applications of DW.
12. A company is using DW to provide quarterly summary information about its
      products and branch sales to the company managers. How many different
      aggregates would be required to fill data cube on product, branches and
      dates if there are 50 products, 5 branches and 7 years of monthly data?
13. Draw starnet query model for above DW (Q. No.12) by considering additional
      3 more dimensions.
14. What is bitmap indexing? What are the benefits of bitmap indexing? Create
      bitmap index for rank and class for the following table:

TID	Rank	Class
T1	First	BE
T2	Second	ME
T3	Distinction	BE
T5	First	HSC
T6	Second	HSC

15. Suppose that a data warehouse for Big University consists of the following
      four dimensions: student, course, semester, and instructor, and two
      measures count and avg grade. What issues are important in building this
      DW?

16. Suppose that a data warehouse consists of the four dimensions, date,
     teams, location, and game, and the two measures, count and charge,
     where charge is the fare that a team pays when playing a game on a
     given date. Teams may be of students, adults, or seniors, with each
     category having its own charge rate.

(a) Draw a star schema diagram for the data warehouse.
(b) Starting with the base cuboid [date, teams, location, game],
      what specific OLAP operations should one perform in order to list
      the total charge paid by student teams at GM_Place in 2004?

Faculty Incharge: Hashmi S A

MGM’s College of Engineering, Nanded
Department of IT
BE (IT) – 2014-15 (Sem2)
MOBILE COMPUTING TERM WORK LIST

1. Design of WLAN.

2. Study of VSAT communication system.

3. Study of Broadcom Wireless device BCM4330.

4. Creation of Ad-hoc WLAN using IEEE 802.11.

5. Creation of Infrastructure –based WLAN using D- Link IEEE 802.11 Access Point DWL 2100AP.

6. Building an application for android phone.

7. To study and create Bluetooth network.

8. To study basic concepts of WAP, WAP architecture, application, etc.

9. To study basic tags of WML.

10. Simulation of WLAN using ns-2.

Faculty Incharge: Hashmi S A

Assignment III Subject: MC Class: BE(IT) Sem II 2014-15

MGM’s College of Engineering, Nanded.
Department of IT
Semester II (2014-15)
Class: BE(IT)       Subject: MC         Assignment III
________________________________________________________

1. Explain different entities of mobile IP with suitable diagram.

2. How does registration on layer 3 of a mobile node work?

3. How tunnelling works in mobile IP using IP-in-IP encapsulation?

4. What is encapsulation? Discuss the advantages and drawbacks of three

    encapsulation techniques in mobile IP.

5. What are the differences between AODV and the standard distance vector

    algorithm?

6. What is minimal encapsulation? Expain with suitable diagram.

7. What are advantages and disadvantages of I-TCP?

8. What  is snooping TCP? What are its benefits?

9. Compare different protocols for mobile transport layer.

10. Calculate the upper bound on TCP’s bandwidth for a network having  

      RTT of 20 ms, error rate of 10^-10  and MSS of 1000 bytes.

11. Give comparison between DSR and AODV algorithms for MANET.

12. Enlist and explain applications of wireless sensor networks.

13. Draw and explain WAP protocol model.

14. How an AP will authenticate mobile node using 802.11 WEP?

15. How authentication is done in Bluetooth network?

16. What is DHCP? Explain the working of DHCP protocol.

Faculty Incharge: Hashmi SA

March 2015 Assignment-II Class: BE(IT) Subject: MC

MGM’s College of Engineering, Nanded.
Department of IT
Semester II (2014-15)
Class: BE(IT)       Subject: MC         Assignment II
________________________________________________________

1. Draw and explain Bluetooth protocol stack.

2. Which physical interface is used in Bluetooth headset? Why?

3. How Bluetooth Piconet and scatternet are formed? Explain.

4. A mobile handset is designed with Bluetooth interface, specify the
    interfaces required for this device. Justify, why you need this interface.

5. How the power is saved in Bluetooth device? Enlist and explain the states of
      Bluetooth devices.

6. What are the functions of LMP and L2CAP in Bluetooth standard?

7. What is authentication? Discuss how it is carried out in Bluetooth security.

8. Define handover. What are the reasons for handover and also explain types of
    handover?

9. Draw and explain system architecture of GSM.

10. Explain the functions of: (i) BSC    (ii) MSC

11. What is the purpose of HLR and VLR in GSM networks?

12. Give the comparison between BTS and BSC.

13. Which functional components are present in mobile station of GSM networks?

14. Why AUC, EIR and OMC are present in OSS of GSM.

15. How security is implemented in GSM networks?

16. Write short note on: GPRS.

Faculty Incharge: Hashmi S A

2014-15 SemII MC Assignment I

MGM’s College of Engineering, Nanded.
Department of IT
Semester II (2014-15)
Class: BE(IT)       Subject: MC         Assignment I
________________________________________________________

1.     Enlist applications of mobile and wireless communication.

2.     What are the advantages and drawbacks of mobile and wireless communication? Explain.

3.     Give comparison between wireless networks and fixed networks.

4.     How mobile communication affects TCP/IP layered model?

5.     What are the benefits and drawbacks of TDMA and FDMA?

6.     What is CDMA? Elaborate its merits and demerits.

7.     What are the different digital modulation techniques? Discuss in brief.

8.     Compare 802.3 and 802.11 standards.

9.     Draw and explain IEEE 802.11 architectures.

10.State and explain the functions of 802.11 layers.

11.Draw and explain IEEE 802.11 DSSS frame format.

12. What are the similarities and differences between 802.11 FHSS and DSSS frame formats?

13. What additional functions are needed for wireless MAC and Why?

14.A video sequence of 1KB is transmitted on 802.11 network. Calculate the size of DSSS frame.

15.  Calculate IP datagram and TCP packet size for the above transmission of Q.14.

16. What are the design goals of wireless LAN? Why?

Faculty Incharge: Hashmi S A