Web Mining Previous Year Questions

Course Title: Web Mining
Course Code: PEC-CSE-405-G
Semester: B.Tech. 7th Semester (CSE)

February—2022 Examination

Short Answer Questions (8 × 1.875 = 15 marks)

1. (a) Define mining.
   (b) Define web spamming.
   (c) Define intelligence.
   (d) Differentiate discrete and continuous optimization.
   (e) Write any two characteristics of artificial bees.
   (f) Discuss the principle of evolutionary computation theory.
   (g) Discuss the term information extraction.
   (h) What are the searching techniques that are commonly used in web search?

UNIT - I (15 marks)

2. (a) How to discover knowledge from hypertext data? Discuss in detail with a suitable example. [8]
   (b) Write down the difference between data mining and web mining. [7]
3. (a) What do you mean by web mining? What are its types? What are the key issues in web mining? [8]
   (b) What are web mining subtasks? Discuss in detail with a suitable example. What are the key challenges in web mining? [7]

UNIT - II (15 marks)

4. (a) What is information retrieval in web mining? What is the reason for having an information retrieval model? What are the steps of the information retrieval process? What type of model is used for text retrieval? [8]
   (b) What is clustering in web mining? What is the function of classification and clustering? How is clustering different from classification? What are the challenges in clustering of the web? [7]
5. What is latent semantic indexing and where can it be applied? How does latent semantic analysis work? How to use latent semantic indexing keywords to boost your SEO? Explain with a suitable example. When we analyze a document using latent semantic analysis (LSA), what are we trying to find? [15]

UNIT - III (15 marks)

6. What is computational intelligence and where is it going? What are the various components of computational intelligence? Discuss its computational models in detail with suitable examples. What is the difference between computational intelligence and artificial intelligence? [15]
7. (a) What is the classification of optimization? What are the types of optimization algorithms? How to choose the right optimization algorithm? [7.5]
   (b) What is swarm intelligence and why is it used? How does swarm intelligence work? What type of multi-agent learning is swarm intelligence? [7.5]

UNIT - IV (15 marks)

8. What is the ant colony optimization technique? Write various types of algorithms in ant colony optimization. What do you do with an anthill? Differentiate ant colony optimization vs particle swarm optimization. [15]
9. (a) Why hybridization? What is the role of hybridization in swarm? How is hybridization calculated using swarm techniques? Discuss with a suitable example. [7]
   (b) Discuss various applications of swarm techniques in different domains and real-world problems with suitable examples. [8]

July—2022 Examination

Short Answer Questions (5 × 3 = 15 marks)

1. Explain the following:
   (a) Data mining
   (b) Latent semantic indexing
   (c) Clustering
   (d) Heuristic algorithms
   (e) Text mining

UNIT - I (15 marks)

2. Define web mining. Explain its various issues and challenges. [15]
3. (a) What are the social impacts of data mining? [7]
   (b) Explain how data mining is used in healthcare analysis. [8]

UNIT - II (15 marks)

4. (a) Differentiate between information retrieval and web search. [8]
   (b) Explain the issues in the process of information retrieval. [7]
5. Explain the components of information retrieval in detail. [15]

UNIT - III (15 marks)

6. How can we classify optimization algorithms? Explain in detail. [15]
7. Explain the following: [15]
   (i) Evolutionary computation theory.
   (ii) Discrete and continuous optimization problems.

UNIT - IV (15 marks)

8. (a) Ant colony optimization. [8]
   (b) Artificial bees algorithm. [7]
9. Discuss various applications of swarm techniques in different real-world problems. [15]

December—2022 Examination

Short Answer Questions (5 × 3 = 15 marks)

1. Explain the following:
   (a) Web mining
   (b) Web spamming
   (c) Collective intelligence
   (d) Continuous optimization problems
   (e) Web content mining

UNIT - I (15 marks)

2. Define data mining. Discuss spatial data mining. [15]
3. (a) Web mining versus data mining. [7]
   (b) Application of data mining in healthcare. [8]

UNIT - II (15 marks)

4. Define information retrieval with the help of its architecture. Give the functions of the information retrieval system. [15]
5. Discuss various information retrieval models in detail. [15]

UNIT - III (15 marks)

6. How are optimization algorithms classified? Explain in detail. [15]
7. (i) Discrete and continuous optimization problems. [8]
   (ii) Social behavior as optimization. [7]

UNIT - IV (15 marks)

8. (a) Particle swarm optimization. [8]
   (b) Firefly algorithm. [7]
9. Discuss various applications of swarm techniques in different real-world problems. [15]

November—2023 Examination

Short Answer Questions (6 × 2.5 = 15 marks)

1. Write short notes on the following:
   (a) Hypertext data
   (b) Text mining
   (c) Latent semantic indexing
   (d) Computational intelligence
   (e) Web spamming
   (f) Information retrieval

UNIT - I (15 marks)

2. (a) Web mining versus data mining. [7.5]
   (b) Discovering knowledge from hypertext data. [7.5]
3. Explain web mining subtasks, issues, and challenges. [15]

UNIT - II (15 marks)

4. Explain different information retrieval models in detail. [15]
5. (a) Web content mining. [7.5]
   (b) Classification of web pages. [7.5]

UNIT - III (15 marks)

6. What are optimization algorithms? Explain in detail. [15]
7. What is computational intelligence? Explain the evolutionary computation theory and its paradigm. [15]

UNIT - IV (15 marks)

8. (a) Swarm optimization. [7.5]
   (b) Artificial bees and firefly algorithms. [7.5]
9. Discuss the applications of swarm techniques in different domains and real-world problems. [15]

May—2024 Examination

Short Answer Questions (6 × 2.5 = 15 marks)

1. Write short notes on the following:
   (a) Web mining
   (b) Information retrieval
   (c) Computational intelligence
   (d) Swarm intelligence
   (e) Web spamming
   (f) Hypertext data

UNIT - I (15 marks)

2. What is data mining? Explain the process to discover knowledge from hypertext data. [15]
3. Explain web mining taxonomy, its issues, and challenges. [15]

UNIT - II (15 marks)

4. Discuss information retrieval models in detail. [15]
5. (a) Latent semantic indexing. [7.5]
   (b) Clustering of web pages. [7.5]

UNIT - III (15 marks)

6. Discuss the discrete and continuous optimization problems in detail. [15]
7. Explain swarm intelligence along with collective intelligence. [15]

UNIT - IV (15 marks)

8. Discuss the ant colony optimization technique in detail. [15]
9. Explain the applications of swarm techniques in different domains and real-world problems. [15]

December—2024 Examination

Short Answer Questions (6 × 2.5 = 15 marks)

1. Explain the following:
   (a) Data mining and web mining
   (b) Text mining
   (c) Web search
   (d) Classification and clustering
   (e) Information retrieval
   (f) Classification of web pages

UNIT - I (15 marks)

2. (a) Discuss the differences and similarities between web mining and data mining. Also, list various applications of web mining. [8]
   (b) How is knowledge discovered from hypertext data, and what are the key challenges involved in the process? [7]
3. (a) Discuss the key issues and challenges faced in web mining. [8]
   (b) What are some common applications of web mining? How do they benefit from web mining techniques to improve decision-making processes? [7]

UNIT - II (15 marks)

4. Discuss various information retrieval models and their role in web search. Explain how these models affect the effectiveness of information retrieval. [15]
5. Explain the following: [3 × 5 = 15]
   (a) Web spamming
   (b) Web content mining
   (c) Latent semantic indexing

UNIT - III (15 marks)

6. What are the key models and concepts of computational intelligence? How can they be applied to solve complex real-world problems? [15]
7. (a) Describe discrete and continuous optimization problems. [8]
   (b) Compare the various types of optimization algorithms, highlighting their key characteristics. [7]

UNIT - IV (15 marks)

8. (a) Ant colony optimization and particle swarm optimization. [8]
   (b) Hybridization and comparisons of swarm techniques. [7]
9. Discuss various applications of swarm intelligence techniques in various domains and provide examples of how they effectively solve real-world optimization problems. [15]