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Web Mining
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Web Mining - is the application of data mining techniques to discover patterns from the
World Wide Web. Web mining can be divided into three different types – Web usage mining,
Web content mining and Web structure mining.
Web usage mining
Web Usage Mining is the application of data mining techniques to discover interesting usage
patterns from Web data in order to understand and better serve the needs of Web-based
applications. Usage data captures the identity or origin of Web users along with their
browsing behavior at a Web site.
Web usage mining itself can be classified further depending on the kind of usage data
considered:

Web Server Data: The user logs are collected by the Web server. Typical data
includes IP address, page reference and access time.

Application Server Data: Commercial application servers have significant features to
enable e-commerce applications to be built on top of them with little effort. A key
feature is the ability to track various kinds of business events and log them in
application server logs.

Application Level Data: New kinds of events can be defined in an application, and
logging can be turned on for them thus generating histories of these specially defined
events. It must be noted, however, that many end applications require a combination
of one or more of the techniques applied in the categories above.
Studies related to work are concerned with two areas: constraint-based data mining
algorithms applied in Web Usage Mining and developed software tools (systems). Costa and
Seco demonstrated that web log mining can be used to extract semantic information
(hyponymy relationships in particular) about the user and a given community.
Pros
Web usage mining essentially has many advantages which makes this technology attractive to
corporations including the government agencies. This technology has enabled e-commerce to
do personalized marketing, which eventually results in higher trade volumes. Government
agencies are using this technology to classify threats and fight against terrorism. The
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predicting capability of mining applications can benefit society by identifying criminal
activities. The companies can establish better customer relationship by giving them exactly
what they need. Companies can understand the needs of the customer better and they can
react to customer needs faster. The companies can find, attract and retain customers; they can
save on production costs by utilizing the acquired insight of customer requirements. They can
increase profitability by target pricing based on the profiles created. They can even find the
customer who might default to a competitor the company will try to retain the customer by
providing promotional offers to the specific customer, thus reducing the risk of losing a
customer or customers.
Cons
Web usage mining by itself does not create issues, but this technology when used on data of
personal nature might cause concerns. The most criticized ethical issue involving web usage
mining is the invasion of privacy. Privacy is considered lost when information concerning an
individual is obtained, used, or disseminated, especially if this occurs without their
knowledge or consent. The obtained data will be analyzed, and clustered to form profiles; the
data will be made anonymous before clustering so that there are no personal profiles. Thus
these applications de-individualize the users by judging them by their mouse clicks. Deindividualization, can be defined as a tendency of judging and treating people on the basis of
group characteristics instead of on their own individual characteristics and merits.
Another important concern is that the companies collecting the data for a specific purpose
might use the data for a totally different purpose, and this essentially violates the user’s
interests.
The growing trend of selling personal data as a commodity encourages website owners to
trade personal data obtained from their site. This trend has increased the amount of data being
captured and traded increasing the likeliness of one’s privacy being invaded. The companies
which buy the data are obliged make it anonymous and these companies are considered
authors of any specific release of mining patterns. They are legally responsible for the
contents of the release; any inaccuracies in the release will result in serious lawsuits, but there
is no law preventing them from trading the data.
Some mining algorithms might use controversial attributes like sex, race, religion, or sexual
orientation to categorize individuals. These practices might be against the anti-discrimination
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legislation. The applications make it hard to identify the use of such controversial attributes,
and there is no strong rule against the usage of such algorithms with such attributes. This
process could result in denial of service or a privilege to an individual based on his race,
religion or sexual orientation. Right now this situation can be avoided by the high ethical
standards maintained by the data mining company. The collected data is being made
anonymous so that, the obtained data and the obtained patterns cannot be traced back to an
individual. It might look as if this poses no threat to one’s privacy, however additional
information can be inferred by the application by combining two separate unscrupulous data
from the user.
Web content mining
Web content mining is the mining, extraction and integration of useful data, information and
knowledge from Web page content. The heterogeneity and the lack of structure that permits
much of the ever-expanding information sources on the World Wide Web, such as hypertext
documents, makes automated discovery, organization, and search and indexing tools of the
Internet and the World Wide Web such as Lycos, Alta Vista, WebCrawler, Aliweb,
MetaCrawler, and others provide some comfort to users, but they do not generally provide
structural information nor categorize, filter, or interpret documents. In recent years these
factors have prompted researchers to develop more intelligent tools for information retrieval,
such as intelligent web agents, as well as to extend database and data mining techniques to
provide a higher level of organization for semi-structured data available on the web. The
agent-based approach to web mining involves the development of sophisticated AI systems
that can act autonomously or semi-autonomously on behalf of a particular user, to discover
and organize web-based information.
Web structure mining
Web structure mining is the process of using graph theory to analyze the node and connection
structure of a web site. According to the type of web structural data, web structure mining can
be divided into two kinds:
1. Extracting patterns from hyperlinks in the web: a hyperlink is a structural component
that connects the web page to a different location.
2. Mining the document structure: analysis of the tree-like structure of page structures to
describe HTML or XML tag usage.
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Web structure mining Terminologies:
1. web graph: directed graph representing web.
2. node: web page in graph.
3. edge: hyperlinks.
4. in degree: number of links pointing to particular node.
5. out degree: Number of links generated from particular node.
Techniques of web structure mining:
1. Page Rank: this algorithm is used by Google to rank search results. The name of this
algorithm is given by Google-founder Larry Page. The rank of a page is decided by
the number of links pointing to the target node.
Web mining in foreign languages
It should be noted that the language code of Chinese words is very complicated compared to
that of English. The GB code, BIG5 code and HZ code are common Chinese word codes in
web documents. Before text mining, one needs to identify the code standard of the HTML
documents and transform it into inner code, then use other data mining techniques to find
useful knowledge and useful patterns.
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