It is Presentation Regarding Basics of Web Data mining in Brief

1. Web Data Mining
Guided by
Prof.A.V.Nandedkar
Presented by
Milind B. Gaikwad
(2016MNS006)

2. Contents :
 Introduction
 Web Mining Taxonomy
 Web Content Mining
 Web Structure Mining
 Web Usage Mining
 Multi-Level Trust PPDM
 Conclusion
 References

3. Introduction :
Definitions :
1)Process Centric View
2)Data Centric View
Web Data:
1)Web pages
2)Intra-page Structures
3)Inter-page Structures
4)Usage Data
5)User Profile and Cookies

4. Web Mining Taxonomy:
Web Content Mining
Web Structure Mining
Web Usage Mining

5. Web Content Mining:
 Definitions
 Problems :
1) Web Information integration and schema matching
2) Opinion extraction from online sources
3) Knowledge Synthesis
4) Segmenting web pages and Detecting noise

6. Web content mining Approaches

7. Techniques of Text Extraction
1) Information Retrieval
It is the science of searching for information in a document, searching for
documents themselves, and also searching for metadata that describe data,
and for databases of texts, images or sounds.
2) Natural Language Processing
is a field of computer science, artificial intelligence concerned with the
interactions between computers and human (natural) languages

8. Web Structure Mining
 Definition
 Types of Web Content Mining
1) Hyperlinks
1) Inter-Document Page Link
https://www.google.co.in/search?q=this+is+hyperlink&oq=this+is+hyperlink&aqs=ch
rome..69i57j0.11470j0j8&sourceid=chrome&ie=UTF-8
2) Intra-Document Page Link
Web Data Mining
2) Document Structure
Web pages can also be Organized in tree Structured Format based on html
and xml Tags

9. Web Content Mining cont…
Document Structure of html

10. Web Usage Mining
 Definition
 Data Sources
 Web Server Data
 Application Server Data
 Application Level Data
Web Usage Mining

11. Continues…
 Modules of Architecture
1) User
2) Security System
3) Fraud User
4) Admin
5)Database

12. DFD of MLT PPDM
Start
Extract The DataBatch Generation
Miner Request
Load The Data
Check
Trust
level
High low Medium
Generates Perturbed copies
Database

13. Conclusion :
 the Expanded PPDM to multilevel trust (MLT) is introduced
and we increases the scope of PPDM, where in existing
system single level trust is available .
 Multilevel Trust Privacy Preserving Data Mining allows to
generate multilevel trust fragmented copies of data for
developed by data owner.

14. References
 1) S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, A novel ultrathin elevated channel low-
temperature poly-Si TFT, IEEE Electron Device Lett., vol. 20, pp. 569571, Nov. 1999.
 2) Yaping Li, Minghua Chen, Qiwei Li, and Wei Zhang IEEE paper on Enabling Multilevel Trust in
Privacy Preserving Data Mining
 3) https://www.youtube.com/watch?v=Ohdm2sQF-as
 4) https://www.youtube.com/watch?v=o67IDJt4_IM
 5) https://www.youtube.com/watch?v=-nFJ6EODe5U
 6)https://www.youtube.com/watch?v=ygwkdmgoy3E
 7) K. Liu, H. Kargupta, and J. Ryan, Random Projection-Based Multiplica-tive Data Perturbation for
Privacy Preserving Distributed Data Mining, IEEE Trans. Knowl-edge and Data Eng., vol. 18, no. 1,
pp. 92-106, Jan. 2006.
 Yaping Li, Minghua Chen, Qiwei Li and Wei Zhang IEEE MANUSCRIPT ACCEPTED FOR
PUBLICATION IN IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2011