Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
What is Web Scraping and What is it Used For? | Definition and Examples EXPLAINED
For More details Visit - https://hirinfotech.com
About Web scraping for Beginners - Introduction, Definition, Application and Best Practice in Deep Explained
What is Web Scraping or Crawling? and What it is used for? Complete introduction video.
Web Scraping is widely used today from small organizations to Fortune 500 companies. A wide range of applications of web scraping a few of them are listed here.
1. Lead Generation and Marketing Purpose
2. Product and Brand Monitoring
3. Brand or Product Market Reputation Analysis
4. Opening Mining and Sentimental Analysis
5. Gathering data for machine learning
6. Competitor Analysis
7. Finance and Stock Market Data analysis
8. Price Comparison for Product or Service
9. Building a product catalog
10. Fueling Job boards with Job listings
11. MAP compliance monitoring
12. Social media Monitor and Analysis
13. Content and News monitoring
14. Scrape search engine results for SEO monitoring
15. Business-specific application
------------
Basics of web scraping using python
Python Scraping Library
Web Scraping and Data Extraction ServicePromptCloud
Learn more about Web Scraping and data extraction services. We have covered various points about scraping, extraction and converting un-structured data to structured format. For more info visit http://promptcloud.com/
The slides for my presentation on BIG DATA EN LAS ESTADÍSTICAS OFICIALES - ECONOMÍA DIGITAL Y EL DESARROLLO, 2019 in Colombia. I was invited to give a talk about the technical aspect of web-scraping and data collection for online resources.
Data Wranglers DC December meetup: http://www.meetup.com/Data-Wranglers-DC/events/151563622/
There's a lot of data sitting on websites just waiting to be combined with data you have sitting on your servers. During this talk, Robert Dempsey will show you how to create a dataset using Python by scraping websites for the data you want.
Start guide to web scraping with Scrapy, one of best python modules to do web scraping, with Scrapy everything is more easy.
This presentation covers the key concepts of scrapy and the process of criation of spiders.
It's the first draft version and will be other versions, until the last version, if you see something that you want to be improved, give feedback and I will take that in consideration.
I also talk about some alternatives to scrapy like lxml, newspapers and others.
In the final i give you acess to the code used on this presentation, so you cant test easy and fast the concepts talked on this presentation.
I hope you like it :D
Web scraping (web harvesting or web data extraction) is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
Advance Clustering Technique Based on Markov Chain for Predicting Next User M...idescitation
According to the survey India is one of the
leading countries in the word for technical education and
management education. Numbers of students are increasing
day by day by the growth rate of 45% per annum. Advancement
in technology puts special effect on education system. This
helps in upgrading higher education. Some universities and
colleges are using these technologies. Weblog is one of them.
Main aim of this paper is to represent web logs using clustering
technique for predicting next user movement and user
behavior analysis. This paper moves around the web log
clustering technique based on Markov chain results .In this
paper we present an ideal approach to web clustering
(clustering web site users) and predicting their behavior for
next visit. Methodology: For generating effective result approx
14 engineering college web usage data is used and an advance
clustering approach is presenting after optimizing the other
clustering approach.Results: The user behavior is predicted
with the help of the advance clustering approach based on the
FPCM and k-mean. Proposed algorithm is used to mined and
predict user’s preferred paths. To predict the user behavior
existing approaches have been used. But the existing
approaches are not enough because of its reaction towards
noise. Thus with the help of ACM, noise is reduced, provides
more accurate result for predicting the user behavior. Approach
Implementation:The algorithm was implemented in MAT
LAB, DTRG and in Java .The experiment result proves that
this method is very effective in predicting user behavior. The
experimental results have validated the method’s effectiveness
in comparison with some previous studies.
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
What is Web Scraping and What is it Used For? | Definition and Examples EXPLAINED
For More details Visit - https://hirinfotech.com
About Web scraping for Beginners - Introduction, Definition, Application and Best Practice in Deep Explained
What is Web Scraping or Crawling? and What it is used for? Complete introduction video.
Web Scraping is widely used today from small organizations to Fortune 500 companies. A wide range of applications of web scraping a few of them are listed here.
1. Lead Generation and Marketing Purpose
2. Product and Brand Monitoring
3. Brand or Product Market Reputation Analysis
4. Opening Mining and Sentimental Analysis
5. Gathering data for machine learning
6. Competitor Analysis
7. Finance and Stock Market Data analysis
8. Price Comparison for Product or Service
9. Building a product catalog
10. Fueling Job boards with Job listings
11. MAP compliance monitoring
12. Social media Monitor and Analysis
13. Content and News monitoring
14. Scrape search engine results for SEO monitoring
15. Business-specific application
------------
Basics of web scraping using python
Python Scraping Library
Web Scraping and Data Extraction ServicePromptCloud
Learn more about Web Scraping and data extraction services. We have covered various points about scraping, extraction and converting un-structured data to structured format. For more info visit http://promptcloud.com/
The slides for my presentation on BIG DATA EN LAS ESTADÍSTICAS OFICIALES - ECONOMÍA DIGITAL Y EL DESARROLLO, 2019 in Colombia. I was invited to give a talk about the technical aspect of web-scraping and data collection for online resources.
Data Wranglers DC December meetup: http://www.meetup.com/Data-Wranglers-DC/events/151563622/
There's a lot of data sitting on websites just waiting to be combined with data you have sitting on your servers. During this talk, Robert Dempsey will show you how to create a dataset using Python by scraping websites for the data you want.
Start guide to web scraping with Scrapy, one of best python modules to do web scraping, with Scrapy everything is more easy.
This presentation covers the key concepts of scrapy and the process of criation of spiders.
It's the first draft version and will be other versions, until the last version, if you see something that you want to be improved, give feedback and I will take that in consideration.
I also talk about some alternatives to scrapy like lxml, newspapers and others.
In the final i give you acess to the code used on this presentation, so you cant test easy and fast the concepts talked on this presentation.
I hope you like it :D
Web scraping (web harvesting or web data extraction) is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
Advance Clustering Technique Based on Markov Chain for Predicting Next User M...idescitation
According to the survey India is one of the
leading countries in the word for technical education and
management education. Numbers of students are increasing
day by day by the growth rate of 45% per annum. Advancement
in technology puts special effect on education system. This
helps in upgrading higher education. Some universities and
colleges are using these technologies. Weblog is one of them.
Main aim of this paper is to represent web logs using clustering
technique for predicting next user movement and user
behavior analysis. This paper moves around the web log
clustering technique based on Markov chain results .In this
paper we present an ideal approach to web clustering
(clustering web site users) and predicting their behavior for
next visit. Methodology: For generating effective result approx
14 engineering college web usage data is used and an advance
clustering approach is presenting after optimizing the other
clustering approach.Results: The user behavior is predicted
with the help of the advance clustering approach based on the
FPCM and k-mean. Proposed algorithm is used to mined and
predict user’s preferred paths. To predict the user behavior
existing approaches have been used. But the existing
approaches are not enough because of its reaction towards
noise. Thus with the help of ACM, noise is reduced, provides
more accurate result for predicting the user behavior. Approach
Implementation:The algorithm was implemented in MAT
LAB, DTRG and in Java .The experiment result proves that
this method is very effective in predicting user behavior. The
experimental results have validated the method’s effectiveness
in comparison with some previous studies.
This Mine Hazard topic is disruption of basic Mining Activity hazard occur during mining operation of Drilling,Blasting,Excavation,Transportation ,Dumping
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
Considerations on the sublevel stoping method; Conditions for application of the deposit; Characteristic of Sublevel Stoping Method; Application; Development; Sublevel overhand; Sublevel underhand; Slot; Configuration of stopes; Drawpoints
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
Automatic recommendation for online users using web usage miningIJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Automatic Recommendation for Online Users Using Web Usage Mining IJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Implementation of Intelligent Web Server Monitoringiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
a novel technique to pre-process web log data using sql server management studioINFOGAIN PUBLICATION
Web log data available at server side helps in identifying user access pattern. Analysis of Web log data poses challenges as it consists of plentiful information of a Web page. Log file contains information about User name, IP address, Access Request, Number of Bytes Transferred, Result Status, Uniform Resource Locator (URL), User Agent and Time stamp. Analysing the log file gives clear idea about the user. Data Pre-Processing is an important step in mining process. Web log data contains irrelevant data so it has to be Pre-Processed. If the collected Web log data is Pre-Processed, then it becomes easy to find the desire information about visitors and also retrieve other information from Web log data. This paper proposes a novel technique to Pre-Process the Web log data and given detailed discussion about the content of Web log data. Each Uniform Resource Locator (URL) in the Web log data is parsed into tokens based on the Web structure and then it is implemented using SQL server management studio.
A Comparative Study of Recommendation System Using Web Usage Mining Editor IJMTER
Web Mining is one of the Developing field in research. Exact custom of the Web is to get the
beneficial material in the sites. To reduce the work time of user the Web Usage Mining (WUM) technique
is introduced. In this Technique use Web Page recommendation for the Web request from the user. For
the recommendation system in Web Usage Mining (WUM) variousauthor has introduce different
Algorithm and technique to improve the user interest in surfing the Web. Web log files are used todefine
the user interest and there next recommend page to view.The data stored in the web log file consist of
large amount oferoded, incomplete, and unnecessary information. So, the Web log files have to preprocess, customize, and to clean the data. In this paper we will survey different recommendation technique
to identify the issues in web surfing and to improve web usagemining (WUM) pre-processing for pattern
mining and analysis.
A Novel Method for Data Cleaning and User- Session Identification for Web MiningIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
Web personalization using clustering of web usage dataijfcstjournal
The exponential growth in the number and the complexity of information resources and services on the Web
has made log data an indispensable resource to characterize the users for Web-based environment. It
creates information of related web data in the form of hierarchy structure through approximation. This
hierarchy structure can be used as the input for a variety of data mining tasks such as clustering,
association rule mining, sequence mining etc.
In this paper, we present an approach for personalizing web user environment dynamically when he
interacting with web by clustering of web usage data using concept hierarchy. The system is inferred from
the web server’s access logs by means of data and web usage mining techniques to extract the information
about users. The extracted knowledge is used for the purpose of offering a personalized view of the
services to users.
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
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Applying web mining application for user behavior understanding
1. APPLYING WEB MINING
APPLICATION FOR USER
BEHAVIOR UNDERSTANDING
Dr. Zakaria Suliman Zubi
Associate Professor
Computer Science Department
Faculty Of Science
Sirte University, Libya
LOGO
3. LOGO
Abstract
Web usage mining (WUM) focuses on the discovering of potential knowledge from
browsing patterns of the users. Which leads us to find the correlation between pages in the
analysis stage.
The primary data source used in web usage mining is the server log-files (web-logs).
Browsing web pages by the user leaves a lot of information in the log-file. Analyzing logfiles information drives us to understand the behavior of the user.
Web log is an essential part for the web mining to extract usage patterns and study the
visiting characteristics of user.
Our paper focus on the use of web mining techniques to classify web pages type according
to user visits.
This classification helps us to understand the user behavior.
We also uses some classification and association rule techniques for discovering the
potential knowledge from the browsing patterns.
5. LOGO
INTRODUCTION
The Internet offers a huge, widely global information center for
News, advertising, consume information, financial management,
education, government, and e-commerce .
The aim of using web mining techniques for understanding user
behavior is to profile user characteristics.
Web mining can be organized into three main categories: web
content mining, web structure mining, and web usage mining.
6. LOGO
INTRODUCTION
Cont..
Web Mining
Web Structure Mining
Web Content Mining
Web Usage Mining
1-Web content mining analyzes web content such as text,
multimedia data, and structured data (within web pages or linked
across web pages).
2 -Web structure mining is the process of using graph and
network mining theory and methods to analyze the nodes and
connection structures on the Web.
3- Web Usage Mining is a special type of web mining tool, which
can discover the knowledge in the hidden browsing patterns and
analyses the visiting characteristics of the users.
7. LOGO
INTRODUCTION Cont..
The Primary Data of Web Usage Mining
1-Web server logs .
2-Data about visitors of the sites.
3-Registration forms.
Fig 2:portion of a typical server log
A standard log-file had the following format
remotehost; logname; username; date; request; status; bytes[ where:
remotehost: is the remote hostname or its IP address;
logname:is the remote log name of the user;
username: is the username with which the user has authenticated himself,
date: is the date and time of the request,
request: is the exact request line as it came from the client,
status: is the HTTP status code returned to the client, and
bytes: is the content-length of the document transferred.
9. LOGO
THE PHASES OF WEB USAGE MINING
Web usage mining is a complete process that
includes various stages of data mining cycle, including
Data Preprocessing, Pattern Discovery & Pattern
Analysis.
Initially, at the data preprocessing stage web log is
preprocessed to clean, integrate and transform into a
common log.
In the pattern discovery: Data mining techniques
are applied to discover the interesting characteristics
in the hidden patterns.
Pattern Analysis is the final stage of web usage
mining which can validate interested patterns from the
output of pattern discovery that can be used to predict
user behavior.
10. LOGO THE PHASES OF WEB USAGE MINING
Data Preprocessing Process
Data Cleaning:
The log-file is first examined to remove
irrelevant entries such as those that represent
multimedia data and scripts or uninteresting
entries such as those that belongs to
top/bottom frames.
Pageview Identification:
Identification of
page views is heavily
dependent on the intra-page structure of the
site, as well as on the page contents and the
underlying site do-main knowledge. each
pageview can be viewed as a collection of
Web objects or resources representing a
specific “user event,”.
Data
Cleaning
Pageview
Identification
User
Identification
Session
Identification
11. LOGO THE PHASES OF WEB USAGE MINING
Data Preprocessing Process
User Identification:
Since several users may share a single
machine name, certain heuristics are
used to identify users . We use the
phrase user activity record to refer to the
sequence of logged activities belonging
to the same user.
Session Identification:
Aims to split the page access of each
user into separated sessions. It defines
the number of times the user has
accessed a web page and time out
defines a time limit for the access of
particular web page for more than 30
minutes if more the session will be
divided in more than one session.
Sample of user and sessions identification
12. LOGO THE PHASES OF WEB USAGE MINING
Pattern Discovery Process:
Discovering user access pattern from the user access log files is the main
purpose of using web usage mining .
Association Rule Mining:
Association rule mining discovery and statistical correlation analysis can
find groups of web pages types that are commonly accessed together
(Association rule mining can be used to discover correlation between pages
types found in a web log) this technique is applied to user and session
identification consisting of item where every item represents a page type ,we
will also use Apriori algorithm to find the correlation between pages based on
the confidence and support vectors.
What are the set of pages type frequently accessed together by the web users.
e.g
(Sport, News, Social)
What the page type will be fetched next.
e.g
Entertainment
13. LOGO THE PHASES OF WEB USAGE MINING
Classification
Classification techniques play an important role in Web analytics
applications for modeling the users according to various predefined
metrics.
In the Web domain, we are interested in developing a profile of users
belonging to a particular class or category . This requires extraction and
selection of features that best describe the properties of a given class or
category.
We will focus also on k-nearest neighbor (K-NN) which was
considered as a predictive technique for classification models. Whereas;
k represents a number of similar cases or the number of items in the
group.
14. LOGO THE PHASES OF WEB USAGE MINING
Pattern Analysis Process:
In this stage of process the discovered patterns will further
processed ,filtered ,possibly resulting in aggregate user models
that can be used as a visualizations tools ,the next figure
summarizes the whole process:
16. RESULTS OF USING ASSOCIATION RULES
LOGO
Log-file in a flat file format.
Import log-file database to our implemented
application.
17. RESULTS OF USING ASSOCIATION RULES
LOGO
Extract the transactional database of
web sever log for every user where
every transaction represents a session.
Find the association rules of user
behavior after applying the Aprori
algorithm to the transactional database of
the user.
19. LOGO
CONCLUSION
We used web data that contained all the information about the user. When
the user leaves accessing the web pages. This data is called web logs or (serverlogs)
A statistical methods such as classification, association rule mining discovery
and statistical correlation analysis which can find groups of web pages types
that are commonly accessed together are applied as well.
Classification is used to map the data item into one of several predefined
classes. The class will belongs into one category such as sport or politics or
education or..etc. We also uses the k-nearest neighbor (K-NN) algorithm as a
common classification method to select the best class.
Association rule mining was used to discover correlation between sites types
found in a web log.
The implemented application program was designed in C# programming
language.