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A SURVEY- LINK ALGORITHM FOR WEB MINING
Gurpreet Kaur 1M.TECH, Research Scholar, Dept. of CSE,
S.G.G.S.W.U., Fatehgarh Sahib (Punjab),India
ggill208@gmail.com
Shruti Aggarwal 1M.TECH, Assistant Professor, Dept. of CSE,
S.G.G.S.W.U., Fatehgarh Sahib (Punjab), India shruti_cse@sggswu.org
Abstract- Web mining is the most active area where the research is going on rapidly. Web mining is
the integration of information gathered by traditional data mining methodologies and techniques
with information gathered over the World Wide Web. Based on the information gathered over the
WWW web mining is categorized into three: Web content mining, Web structure mining and Web
usage mining. In search engines web mining application can be seen. Most of the search engines are
ranking their search results in response to user’s queries to make their search navigations easier. In
this paper we give a survey of page ranking algorithms and description about Weighted Page
Content Rank (WPCR) based on web content mining and structure mining that shows the relevancy
of the pages to a given query is better determined, as compared to the Page Rank and Weighted Page Rank algorithms.
Keywords- Web mining, web content, Page rank, Weighted Page rank, weighted page content rank, web
structure.
I INTRODUCTION
The World Wide Web is the collection of
information resources on the Internet that are using the Hypertext Transfer Protocol. It
is a repository of many interlinked hypertext
documents, accessed via the Internet. Web
may contain text, images, video and other
multimedia data. In order to analyze such
data, some techniques called web mining
techniques are used by various web
applications and tools. Web mining
describes the use of data mining techniques
to automatically discover Web documents
and services, to extract information from the
Web resources and to uncover general
patterns on the Web. Over the years, Web
mining research has been extended to cover
the use of data mining and similar
techniques to discover resources, patterns,
and knowledge from the Web-related data (such as Web usage data or Web server
logs).It is used to understand customer
behavior, evaluate the effectiveness of a
particular Web and help quantify the success
of a marketing campaign. It is a rapidly
growing research area.
II Web Mining
In 1996 it’s Etzioni who first coined the
term web mining. Etzioni starts by making a
hypothesis that information on web is
sufficiently structured and outliers the
subtasks of web mining.[5].It refers to
overall process of discovering potentially
useful and previously unknown information
from web document and services web
mining could be viewed as an extension of
standard data mining to web data.
Fig.4. Taxonomy of web mining [8].
2.1.Web Mining Process
The complete process of extracting
knowledge from Web data is as follows:
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Web mining can be decomposed into the
subtasks, namely:
Resource finding:- the task of retrieving
intended Web documents. By resource
finding we mean the process of retrieving
the data that is either online or offline from
the text sources available on the web such as
electronic newsletters, electronic newswires,
the text contents of HTML documents
obtained by removing HTML tags, and also
the manual selection of Web resources.
Information selection and pre-
processing:- automatically selecting and
preprocessing specific information from
retrieved Web resources. It is a kind of
transformation process of original data
retrieved in the IR process. These
transformations could be either a kind of
pre-processing aimed at obtaining the
desired representation such as finding
phrases in the training corpus, transforming
the representation to relational or first order
logic form, etc.
Generalization:- automatically discovers
the general patterns at individual Websites
as well as across multiple sites. Machine
learning or data mining techniques are
typically used in the process of generalization. Humans play an important
role in the information or knowledge
discovery process on the Web since the Web
is an interactive medium.
Analysis:- validating and/or interpretation
of the mined patterns.
A. Web Content Mining
Web Content Mining [9] deals with
discovering useful information or
knowledge from web page contents. Web
content mining analyzes the content of web
resources. Content data is the collection of
facts that are contained in a web page. It
consists of unstructured data such as free
texts, images, audio, video, semi structured
data such as HTML documents and a more
structured data such as data in tables or
database generated HTML pages. The
primary web resources that are mined in
web content mining are individual pages.
They can be used to group, categorize,
analyze and retrieved documents.
B. Web Structure Mining
Web structure Mining[10] is the process of
discovering structure information from the
web. The structure of a typical web graph
consists of web pages as nodes and
hyperlinks as edges connecting related
pages.
Fig.5. Web Graph Structure.[12].
a.) Hyperlinks: A hyperlink is a
structural unit that connects a location in a
web page to a different location, either
within the same web page or on a different
web page. A hyperlink that connects to a
different part of the same page is called an
intra-document hyperlink, and a hyperlink
that connects two different pages is called an
inter-document hyperlink.
b.) Document structure: In addition,
the content within a web page can also be
organized in a tree structured format, based
on the various HTML and XML tags within
the page. Mining efforts here have focused
on automatically extracting document object
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model structures out of documents.
C. Web Usage Mining
Web usage mining[11] is the process of
finding out what users are looking for on the
internet. Web usage mining focuses on the
techniques that could predict the behavior of
users while they are interacting with the
WWW. It collects the data from web log
records to discover user access patterns of
web pages. Usage data captures the identity
or origin of web users along with their
browsing behavior at a website. There are
two main tendencies in web usage mining
driven by the application of the discoveries:
General Access Pattern Tracking and
Customized Usage Tracking. The general
access pattern tracking analysis the web logs
to understand access patterns and trends. Its
purpose is to customized websites to the
users.
III Link Analysis Algorithms
Web mining technique provides the additional information through hyperlinks
where different documents are connected.
We can view the web as a directed labeled
graph whose nodes are the documents or
pages and edges are the hyperlinks between
them. This directed graph structure is known
as web graph. There are number of
algorithms proposed based on link analysis.
Three important algorithms Page Rank,
Weighted Page Rank and Weighted Page
Content Rank are discussed below:
A. Page Rank
This algorithm was developed by Brin and
Page Stanford University which extends the
idea of citation analysis. In citation analysis
the incoming links are treated as citation but
this technique could not provide fruitful
results because this gives some
approximation of importance of page. So,
page provides a better approach that can
compute the importance of web page by
simply counting the number of pages that
are linked to it. These links are called
backlinks. If a backlinks comes from an
important page than this link is given higher
weightage than those which are coming
from non important pages. The link from
one page to another page is considered as a
vote. Not only the number of votes that a
page receives is important but the
importance of pages that casts the vote is
also important. Page and Brin proposed a
formula to calculate the page rank of a page
A as stated below:
PR(A)=(1-d)+d(PR(T1)/C(T1)+…..+PR(Tn/C(Tn))
Here PR(Ti) is the page rank of the pages Ti
which links to page A, C(Ti) is number of
outlinks on page Ti and d is damping factor.
It is used to stop other pages having too
much influence. The total vote is “damped
down” by multiplying it to 0.85.
The page rank forms a probability
distribution over the web pages so the some
of page ranks of all web pages will be one.
The page rank of a page can be calculated
without knowing the final value of page
rank of other pages. It is an interactive
algorithm which follows the principle of
normalized link matrix of web. Page rank of
a page depends on the number of pages
pointing to a page.
B. Weighted Page Rank
The more popular web pages are the more
linkages that other web pages tends to have
to them or are linked to by them. The
proposed extended page rank algorithm –a
weighted page rank algorithm assigns larger
rank values to more important pages instead
of dividing the rank value of a page evenly
among its outlink pages. Each out link page
gets a value proportional to its popularity.
The popularity from the number of inlinks
and out links are recorded as Win(v,u) and
Wout
(v,u) respectively. Win(v,u) is the
weight of link(v,u) calculated based on the
number of in links of page u and the number
of inlinks of all reference pages of page v.
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Iu
Win
(v,u) = ∑p € R(v) Ip
Where Iu and Ip represent the number of in
links of pages u and page p, respectively.
R(v) denotes the reference page list of page
v. Wout
(v,u) is the weight of link(v,u)
calculated based on the number of out links
of page u and the number of out links f all
reference page of page v.
Wout
(v,u) = Ou
∑ p € R(v) Op
Where Ou and Op represent the number of
outlinks of the page u and page p,
respectively. R(v) denotes the reference
page list of page v.
C. Weighted Page Content Rank
Weighted Page Content Rank Algorithm is
a proposed page ranking algorithm which is
used to give a sorted order to the web pages
returned by a search engine in response to a
user query. WPCR is a numerical value
based on which the web pages are given an
order. This algorithm employs web structure
mining as well as web content mining
techniques. Web structure mining is used to
calculate the importance of the page and
web content mining is used to find how
much relevant a page is? Importance here
means the popularity of the page i.e. how
many pages are pointing to or are referred
by this particular page. It can be calculated
based on the number of in links and out
links of the page. Relevancy means
matching of the page with the fired query. If
a page is maximally matched to the query
that becomes more relevant.
Algorithm of Weighted Page Content
Rank
Input: Page P, Inlink and Outlink Weights
of all backlinks of P, Query Q, d (damping
factor).
Output: Rank score
Step 1: Relevance calculation:
a) Find all meaningful word strings of Q
(say N)s
b) Find whether the N strings are occurring
in P or not? Z= Sum of frequencies of all N strings.
c) S= Set of the maximum possible strings
occurring in P.
d) X= Sum of frequencies of strings in S.
e) Content Weight(CW)= X/Z
f) C= No. of query terms in P
g) D= No. of all query terms of Q while
ignoring stop words.
h) Probability Weight(PW)= C/D
Step 2: Rank calculation:
a) Find all backlinks of P (say set B).
b)PR(P)=(1-d)+d[ PR(V) W
in(P,V)W
out(P,V) ](CW+PW)
c) Output PR(P) i.e. the Rank score
Comparison of Algorithms Table shows the difference between above
three algorithms:
Table : Comparison of Page Rank, Weighted
Page Rank and Weighted Page Content
Rank
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IV Conclusion Web mining is the Data Mining technique
that automatically discovers or extracts the
information from web documents. Page
Rank and Weighted Page Rank algorithms
are used in Web Structure Mining to rank
the relevant pages.In this paper we focused
on comparitative study of page rank Algorithms .By using Page Rank and
Weighted Page Rank algorithms users may
not get the required relevant documents
easily, but in new algorithm Weighted Page
Content Rank user can get relevant and
important pages easily as it employs web
structure mining and web content mining.
As part of our future work, we are planning
to implement the Weighted Page Content
Rank algorithm and integrate it with
clustering algorithm and working on
finding required relevant and important
pages more easily.
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