IGNOU MCSL 223 SOLVED ASSIGNMENT

Included:

MCSL 223 (January 2025 - July 2025) - ENGLISH

Course Code: MCSL-223 

Course Title:Computer Networks and Data Mining Lab

MCA_NEW(II)/L-223/Assign/2024-25

Assignment Number Maximum Marks : 100

Weightage:30%

Last Dates for Submission:

31st October, 2024 (For July, 2024 Session) 15th April, 2025 (For January, 2025 Session)

The assignment has two parts A and B. Answer all the questions. Each part is for 20 marks. Computer Networks and Data Mining lab record carries 40 Marks. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the MCA(New) Programme Guide for the format of presentation. If any assumptions made, please state them.

PART-I: Computer Networks

Q1: Setup 4 nodes , two TCP client and server pair and two UDP client and server pair. Send packets to respective clients from both the servers. Monitor the traffic for both the pair and plot the number of bytes received. Make necessary assumptions.

PART-II: Data Mining Lab

 

,Q1: Implement simple K-Means Algorithm to demonstrate the clustering rule on the following datasets:

(a) iris.arff 

(b) student.arff

,

MCSL 223 (July 2025 - January 2026) - ENGLISH

Course Code

MCSL-223

Course Title

Computer Networks and Data Mining Lab

Assignment Number

MCA_NEW(II)/L-223/Assign/2025-26

Maximum Marks

100

Weightage

30%

Last Dates for Submission:

31 October, 2025 (For July, 2025 Session) 15th April, 2026 (For January, 2026 Session)

The assignment has two parts A and B. Answer all the questions. Each part is for 20 marks. Computer Networks and Data Mining lab record carries 40 Marks. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the MCA(New) Programme Guide for the format of presentation. If any assumptions made, please state them.

PART-I: Computer Networks

Q1: Create a simple network topology having two client nodes on left side and two server nodes on the right side. Both clients are connected with another node nl. Similarly, both server nodes connecting to node n2. Also connect nodes nl and n2 thus forming a dumbbell shape topology. Use point to point links only. Make necessary assumptions.

(20 Marks)

PART-II: Data Mining Lab

Q1:Implement Hierarchical Clustering Algorithm to demonstrate the clustering rule process in the following datasets:

(a) employee.arff

(b) student.arff

 

MCSL 223 (July 2026 - January 2027) - ENGLISH

Course Code : MCSL-223
Course Title : Computer Networks and Data Mining LabAssignment Number : MCA_NEW/MCAOL(II)/L-223/Assign/2026-27Maximum Marks : 100
Weightage : 30%
Last Dates for Submission : 31st October, 2026 (For July, 2026 Session)
15th April, 2027 (For January, 2027 Session)
The assignment has two parts A and B. Answer all the questions. Each part is for 20 marks. Computer Networks and Data Mining lab record carries 40 Marks. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the MCA(New) Programme Guide for the format of presentation. If any assumptions made, please state them.
PART-I: Computer Networks
The following questions may be implemented using any open-source computer network programming platform, like Network Simulator-3 (NS-3).
Q1: Create a network consisting of 4 nodes connected using Point-to-Point links. Configure IP addresses, install the Internet stack, and transmit packets from Node 1 to Node 4. Verify successful communication using the simulation output. Make any further suitable assumptions, wherever necessary.
Q2: Design a network of 6 nodes where communication between the source and destination requires at least 3 hops. Configure Point-to-Point links and demonstrate successful packet transmission. Also, measure the end-to-end delay and packet delivery.
Q3: Construct a topology with two different paths between a source node and a destination node. Assign different delays or bandwidths to the links. Simulate packet transmission and observe which path is selected. Also, modify one link parameter and compare the results.
Q4: Create a network of 7 interconnected nodes, and simulate a broadcast packet transmission from one node and observe how packets propagate through the network. Compare the number of transmissions with a manually constructed logical spanning tree, and comment on the reduction in redundant transmissions.
PART-II: Data Mining Lab
There is equal marks weightage in all sub-parts of a question.
Q1: Implement the K-Means Clustering algorithm on the following datasets and analyze the generated
clusters. Comment on the suitability of the selected number of clusters.
2
a. Customer Purchase dataset with attributes: Customer ID, Age, Gender, Annual Income,
Spending Score, Membership Type, and Number of Purchases.
b. Student Performance dataset with attributes: Student ID, Attendance Percentage, Internal
Assessment Marks, Assignment Score, Study Hours per Week, and Final Grade.
Assume creating the above files in .csv format and prepare it as .arff file. You may process the files and apply the clustering mechanism using WEKA.
Q2: Apply the Apriori Association Rule Mining algorithm on the following transactional datasets. You may generate frequent itemsets and association rules by selecting suitable minimum support and confidence values. Also, provide interpretation of the discovered patterns in all following cases:
a. Retail Store Transaction with attributes: Transaction ID, Customer ID, Item Purchased, Product Category, Quantity, Day of Purchase, and Payment Mode. You may create a retail file or any in-built retail.arff file can be used.
b. Using WEKA, apply the Apriori algorithm to a market basket dataset. Experiment with different minimum support and confidence values, and compare the generated association rules.

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