The document discusses web mining, which involves applying data mining techniques to discover useful information and patterns from web data. It covers the types of web data, various applications of web mining, challenges, and different techniques used. These include classification, clustering, association rule mining. It also discusses how web mining can be used to solve search engine problems and how cloud computing provides a new approach for web mining through software as a service.

1. By: Shireen Fatima ()
Guide: Dr. Siddhartha Ghosh

2. 
Web Mining :Accomplishments & Future Directions
by Jaideep Srivastava

Mining the Web: Discovering Knowledge from
Hypertext Data by Soumen Chakrabati

Web Mining today and tomorrow by Kavita Sharma
and Vikas Kumar

3. 
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Introduction
Applications
Challenges
Web Mining taxonomy
Solution to Search Engine Problem
Web Mining through cloud computing
Conclusion

4. 
Data mining: turn data into knowledge.

Web mining is the application of data mining
techniques to find interesting and potentially
useful knowledge from web data.

5. Web data is

Web content –text,image,records,etc.

Web structure –hyperlinks,tags,etc.
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Web usage –http logs,app server logs,etc.

6. 
Personalized customer experience in ecommerce - Amazon.com

Web Search- Google

Web wide tracking - Double Click

Understanding Web communities- AOL

Understanding auction behavior - eBay
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Personalized Portal for the Web - My Yahoo

7. 
Information filtering techniques try to learn
about users’ interests based on their evaluation
and actions, and then to use this information to
analyze new documents.

It Increase the value of each visitor. Improve the
visitor’s experience at the websites.

Web mining is attractive for companies, because
of several advantages.In the most general sense
it can contribute to the increase of profit.

8. Information is Huge.
Information is diverse.
 Information is redundant

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10. 
Discovery of useful information from web
contents / data / documents

The data mining techniques applied are:
Classification
Clustering
Associations
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11. 
Given:
-A source of textual
documents.
-Similarity measure
e.g., how many words
are common in these
documents?
•
Find:
Several clusters of documents
that are relevant to each other

12. 
Association Rules:
discovers
similarity
transactions
among
X =====> Y
where X,Y are sets of items,
sets
of
items
across
confidence or P(X v Y),
support or P(X^Y)
 Classification: is the task of generalizing known
structure to apply to new data.
 For example, an e-mail program might attempt to
classify an e-mail as "legitimate" or as "spam".

13. 
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The structure of a typical Web graph consists of Web
pages as nodes, and hyperlinks as edges connecting
between two related pages.
Web Structure Mining is the process of discovering
structure information from the Web.
Web-graph: A directed graph that represents the Web.
‰
Node: Each Web page is a node of the Web-graph.
‰
Link: Each hyperlink on the Web is a directed edge of
the Web-graph

14. 
It deals with understanding user behavior in
interacting with the web or with a website.

To obtain information that may assist web
sites for reorganization or adaptation to
better suit the user.

16. 
Clustering and Classification
 clients who often access /products/software/webminer.html
tend to be from educational institutions.
 clients who placed an online order for software tend to be
students in the 20-25 age group
 75% of clients who download software from
/products/software/demos/ visit between 7:00 and 11:00
pm on weekends

17. Sequential patterns - A set of items is followed by
another item in time-order
Web usage examples
30% of clients who visited /products/software/, had done a
search in Yahoo using the keyword “software” before their
visit
60% of clients who placed an online order for WEBMINER,
placed another online order for software within 15 days

18. 
As the search engines use enormous information
existing in the web sites, web pages, it is a
challenging task to engineer, implement and to
improvise the search engine.

It helps in problems of how to effectively deal with
uncontrolled hypertext collection where anyone can
publish anything they want.

19. 
Web Mining Applications have been used by the
web sites such as Web search e.g., Google and
Yahoo ,Web Recommendations e.g., Amazon.com ,
Web Advertising e.g., Google and Yahoo.

Web site design e.g., landing page optimization

20. 
Cloud Computing is clearly one of today's most
seductive technology areas due at least in part to its
cost efficiency and flexibility.

Cloud Mining is new approach to faced search
interface for your data. SaS (Software-as-a-Service)
is used for reducing the cost of web mining and try
to provide security that become with cloud mining
technique.

21. 
Web Mining fills the information gap between web users
and web designers

Many successful techniques have been developed for the
mining the web

Cloud mining is the improvised method for web mining
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The need for discovering new methods and techniques to
handle the amounts of data existing in this universe will
always exist.