Web Data Mining
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
MIE-DDW.16 | Z,ZK | 5 | 2P+1C | English |
- Course guarantor:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Software Engineering
- Synopsis:
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A student learns in detail about various search and data mining methods and will be able to select, in the context of a given application, a suitable method of automatic web data processing and to control the process of its usage.
- Requirements:
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Familiarity with basic principles of WWW data representation, such as the HTML language.
- Syllabus of lectures:
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1. Main topic of the web mining: Web Content Mining, Web Structure Mining, and Web Usage Mining.
2. An overview of practical web mining applications.
3. Web Content Mining: document indexing and retrieval in the web environment, Boolean and vector retrieval models, latent semantic indexing (LSI), results ordering, meta-search.
4. Web Content Mining: web documents categorization and clustering.
5. Natural Language Processing methods used for web information retrieval: lemmatization, part-of-speech tagging, disambiguation, shallow syntactic parsing, etc.
6. Web Structure Mining: primary web browsing (crawling, spidering), link topology analysis, PageRank, HITS methods.
7. Global analysis of the Web; social networks analysis.
8. Web Usage Mining: mining for user behavior on the web, internet marketing.
9. Information Extraction as a specific type of web content mining: wrapper-based vs. token activated extraction.
10. Specific applications: opinion mining vs. fact mining, web spam analysis, comparative shopping, etc.
11. Web information integration, mapping schemas usage.
12. Web Mining and its relation to the Semantic Web: automatic semantic annotation, ontology learning, Semantic Web search.
- Syllabus of tutorials:
- Study Objective:
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Provide students with an overview of web mining technologies and qualify them to use some of them in practice.
- Study materials:
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1. Chakrabarti, S. ''Mining the Web: Discovering Knowledge from Hypertext Data''. Morgan Kaufmann, 2002. ISBN 1558607544.
2. Konchady, M. ''Building Search Applications: Lucene, LingPipe, and Gate''. Mustru Publishing, 2008. ISBN 0615204252.
- Note:
- Further information:
- https://courses.fit.cvut.cz/MI-DDW/
- No time-table has been prepared for this course
- The course is a part of the following study plans: