Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information. Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified. Data mining is about technique for finding and describing Structural Patterns in data. Data mining is the process of finding correlation or patterns among fields in large relational databases. The process of extracting valid, previously unknown, comprehensible , and actionable information from large databases and using it to make crucial business decisions.

1. Mining WWW
Abstract—Web mining is a very hot research topic
which combines two of the activated research
areas: Data Mining and World Wide Web. The
Web mining research relates to several research
communities such as Database, Information
Retrieval and Artificial Intelligence. Although
there exists quite some confusion about the Web
mining, the most recognized approach is to
categorize Web mining into three areas: Web
content mining, Web structure mining, and Web
usage mining.
III. DIFFERENT TYPES OF DATA MINING:
Business Data Mining.
Scientific Data Mining.
Internet Data Mining.
IV. MAJOR ELEMENTS OF DATA MINING:
Extract, Transform and load transaction
data on to the data warehouse system.
I. INTRODUCTION:
Data mining refers to the process of analysing the data
from different perspectives and summarizing it into
useful information.
Store
and
manage
data
multidimensional database system.
Data mining software is one of the number of tools
used for analysing data. It allows users to analyse from
many different dimensions and angles, categorize it,
and summarize the relationship identified.
in
Provide access to business analysts and
information technology Professionals.
Analyse the data by application software.
Data mining is about technique for finding and
describing Structural Patterns in data.
Present the data in useful format such as
graph or table.
V. REQUIREMENTSOF DATA MINING.
II. DEFINITION:
Handling of different types of data.
Data mining is the process of finding correlation or
patterns among fields in large relational databases.
Efficiency and scalability of algorithm.
The process of extracting valid, previously unknown,
comprehensible , and actionable information from
large databases and using it to make crucial business
decisions. (Simousis 1996).
Usefulness, certainty and expressiveness
of results.
Expression of various kinds of mining
results.
Interactive mining knowledge at multiple
levels.
Mining information
sources of data.
from
different
Protection of privacy and data security.
Fig: 1 – Stages of Data Processing.
VI.
VARIOUS KINDSOFDATA ON WHICH
DATA MININGIS APPLIED:

2. Relational database.
Data warehouse.
Transactional database.
Multimedia database
Spatial and temporal data.
Object – relational database.
VII. DATA MININGAPPLICATION:
scalability, multimedia and temporal data respectively,
due to those situations; the users are currently
“drowning” in an information overload that expands at
rate that far outpaces human ability to process and
exploit it.
IX. DOMAINS FOR WEB MINING:
The main application for Data Mining is Web Mining.
There are three domains that pertain to Web mining.
What is Web Mining?
“Web mining can be broadly defined as the automated
discovery and analysis of useful information from
documents and services using data mining
techniques.”
Web mining is the application of data mining or other
information process techniques to WWW, to find
useful patterns. People can take advantage of these
patterns to access WWW more efficiently.
Data Mining, also popularly known as Knowledge
Discovery in Databases (KDD).
Fig 3: Three domains to Web mining
Web content mining.
Web structure mining.
Web usage mining
Fig 2: Web Mining
VIII.
NEED FOE WEB MINING:
Now a day, the World Wide Web is a popular and
interactive medium, ideal for publishing information.
It is huge, diverse and dynamic and thus raises issue of

3. These metadata, are organized into structural
collections (Eg : relational or object – oriented
databases) and can be analyzed.
b.
WEB STRUCTURE MINING:
The data which describes organizations of content.
Intra – page structure information includes the
arrangement of various HTML or XML tags within a
given page. This can be represented as tree structure,
where the <html> tag becomes the root of the tree.
Fig 4: Three domains of Web mining in detail
a.
The principal kind of inter – page structure
information is hyper – links connecting one page to
another.
WEB CONTENT MINING:
c.
Web content mining is an automatic process that
extracts patterns from on – line information, such as
the HTML files, images, or Emails, and it already goes
beyond only keywords extraction or some simple
statistics of words and phrases in documents.
Web content mining is the “process of information or
resource discovery from millions of source across the
World Wide Web”.
WEB USAGE MINING:
Web servers record and accumulate data about user
interaction whenever requests for resources are
received.
Analysing the web access logs of different Web sites
can help to understand the user behaviour and the Web
structure, by improving the design of the colossal
collection of resources.
There are two approaches in web content mining:
X. WEB MINING TECHNIQUES:
Agent – based approaches.
The agent based approach involves artificial
intelligence system that can “act autonomously or
semi – autonomously on behalf of a particular user, to
discover and organize Web – based information.”
Some intelligent Web agents use a user profile to
search for relevant information then organize and
interpret the discovered information. (Eg : Harvest).
The common techniques for web mining are:
Clustering / Classification.
Association.
Path analysis.
Sequential patterns.
CLUSTERING / CLASSIFICATION.
Data approaches.
The database approach focuses on “integrating and
organizing the heterogeneous and semi – structured
data on the Web into more structured and high level
collections of resources.”
The technique is used to develop profiles of items with
similar characteristics.
This ability enhances the discovery of relationships
that are otherwise not obvious. Eg : Classification of
Web access logs allows a company to discover the
average age of customers who order a certain product.

4. XII. CURRENT RESEARCH:
Association Rules.
Rules that govern “databases of transactions where
each transaction consists of a set of items.”
This technique is used to predict the correlation of
items “where the presence of one set items in a
transaction implies (with a certain degree of
confidence.) the presence of other items.”
Path Analysis.
A technique that involves the generation of some form
of graph that “represents relation[s] defined on Web
pages.”
This can be the physical layout of a Web site in which
the Web pages are nodes and the hypertext links
between these pages are directed edges.
Eg : What paths do users travel before they go to a
particular URL.
Sequential Patterns.
Applied to Web access server transaction logs. The
purpose is to discover sequential patterns that indicates
user visit patterns over a certain period.
XI. WEB MINING AS A TOOL:
Web mining can be a promising tool address
ineffective search engine, which produce incomplete
indexing, unverified reliability of retrieved
information.
Web mining discovers information from mounds of
data on the WWW, but it also monitors and predicts
user visit habits. This gives designers more reliable
information.
Web mining technology can help librarians design
Web sites with path that can be travelled easily by end
user, saving time and efforts.
Eg:
Web
librarianship.
miningtechnology
and
academic
As many researchers believe, it was Etzioni who first
came up with the term of Web mining in his paper .
He brought out a question: is it practical to mine Web
data? He also suggested dividing the Web mining to
three processes. The paper opened up a new active
research field.
There are increasing number of researcher working on
this field and do some surveys around the data mining
on the Web. The Web mining was clearly categorized
as Web content mining, Web structure mining and
Web usage mining in till 1999. The research works
have been well classified since then.
There have been some works around content mining,
and structure mining, based on the research of Data
mining and Information Retrieval, Information
Extraction, and Artificial Intelligence.
In the usage mining research area, several groups did
distinguished work. R. Cooley et al. in University of
Minnesota did in-depth research to all the procedure of
usage
mining.
They
proposed
a
mining
prototypeWebMiner and derived a system WebSIFT
to perform the usage mining, which is relatively
practical. O. Zaiane et al. proposed the idea of how to
implement the OLAP technique on the Web mining.
Their works on the multimedia data also provided a
valuable solution for content mining. M. Spiliopoulou
et al. focused on the applications of the usage mining.
His works on the navigation pattern discovery and
web site personalization has special meaning for the ecommerce society and the Web marketplace
allocation, and will be very helpful for both Web user
and administrator. The Web Utilization Miner system
is aninnovative sequential mining system.
J. Borges et al. has explored some algorithms to mine
the user navigation pattern in and his other papers. He
proposed a data mining model to achieve an efficient
mining, which captures the user navigation behaviour
pattern by using Ngrammar approach.

5. REFERENCES:
[1] www.datawarehousingonline.com
[2] Data base System – Elmasri, Navathe.
Data Mining Technologies – Arun K Pujari.
[3]http://www.cse.aucegypt.edu/~rafea/CSCE564/
sldes/WebMiningOverview.pdf
[4]https://cs.uwaterloo.ca/~tozsu/courses/cs748t/s
urveys/wang.pdf
[5] http://www.jatit.org/volumes/researchpapers/Vol18No1/10Vol18No1.pdf
Fig 5: Web Mining Architecture.
[6] http://www.mozenda.com/web-miningsoftware
XIII. MINING TOOL:
Mozenda
Mozenda is a Software as a Service (SaaS) company
that enables users of all types to easily and affordably
extract and manage web data. With Mozenda, users
can set up agents that routinely extract data, store data,
and publish data to multiple destinations. Once
information is in the Mozenda systems users can
format, repurpose, and mashup the data to be used in
other online/offline applications or as intelligence. All
data in the Mozenda system is secure and is hosted in
class A data warehouses but can be accessed over the
web securely via the Mozenda Web Console. With the
addition of a fully featured REST API, Companies can
now seamlessly integrate their data automation with
the Mozenda application.
CONCLUSION:
Data warehousing provides the means to change the
raw data into information for making effective
business decisions – the emphasis on information, not
data.
The Data warehouse is the hub for decision support.
Data mining is a useful tool with multiple algorithms
that can be tuned for specific tasks. It benefits
business, medical, and science.