scholarly journals Web Personalization With Web Usage Mining Technics and Association Rules

2015 ◽  
Vol 15 (1) ◽  
pp. 6394-6401 ◽  
Author(s):  
G. Kazeminuri ◽  
A. Harounabadi ◽  
J. Mirabedini
Keyword(s):  
Association Rules ◽  
Web Mining ◽  
Information Overload ◽  
Relevant Information ◽  
Web Personalization ◽  
Efficient Manner ◽  
Leading Role ◽  
Hidden Knowledge ◽  
Access To Data ◽  
The Web

As amount of information and web development increase considerably, some technics and methods are required to allow efficient access to data and information extraction from them. Extracting useful pattern from worldwide networks that are referred to as web mining is considered as one of the main applications of data mining. The key challenges of web users are exploring websites for finding the relevant information by taking minimum time in an efficient manner. Discovering the hidden knowledge in the manner of interaction in the web is considered as one of the most important technics in web utilization mining. Information overload is one of the main problems in current web and for tackling this problem the web personalization systems are presented that adapts the content and services of a website with user's interests and browsing behavior. Today website personalization is turned into a popular event for web users and it plays a leading role in speed of access and providing users' desirable information. The objective of current article is extracting index based on users' behavior and web personalization using web mining technics based on utilization and association rules. In proposed methods the weighting criteria showing the extent of interest of users to the pages are expressed and a method is presented based on combination of association rules and clustering by perceptron neural network for web personalization. The proposed method simulation results suggest the improvement of precision and coverage criteria with respect to other compared methods.

Mining for Web Personalization

Web Mining ◽  
2011 ◽  
pp. 27-49 ◽  
Author(s):  
Penelope Markellou ◽  
Maria Rigou ◽  
Spiros Sirmakessis
Keyword(s):  
Web Mining ◽  
Information Overload ◽  
Close Relation ◽  
Access To Information ◽  
Web Personalization ◽  
Current Trends ◽  
Technical Issues ◽  
Human Capability ◽  
Scientific Results ◽  
The Web

The Web has become a huge repository of information and keeps growing exponentially under no editorial control, while the human capability to find, read and understand content remains constant. Providing people with access to information is not the problem; the problem is that people with varying needs and preferences navigate through large Web structures, missing the goal of their inquiry. Web personalization is one of the most promising approaches for alleviating this information overload, providing tailored Web experiences. This chapter explores the different faces of personalization, traces back its roots and follows its progress. It describes the modules typically comprising a personalization process, demonstrates its close relation to Web mining, depicts the technical issues that arise, recommends solutions when possible, and discusses the effectiveness of personalization and the related concerns. Moreover, the chapter illustrates current trends in the field suggesting directions that may lead to new scientific results.

Mining for Web Personalization

2011 ◽  
pp. 77-97
Author(s):  
Penelope Markellou ◽  
Maria Rigou ◽  
Spiros Sirmakessis
Keyword(s):  
Web Mining ◽  
Information Overload ◽  
Close Relation ◽  
Access To Information ◽  
Web Personalization ◽  
Current Trends ◽  
Technical Issues ◽  
Human Capability ◽  
Scientific Results ◽  
The Web

The Web has become a huge repository of information and keeps growing exponentially under no editorial control, while the human capability to find, read and understand content remains constant. Providing people with access to information is not the problem; the problem is that people with varying needs and preferences navigate through large Web structures, missing the goal of their inquiry. Web personalization is one of the most promising approaches for alleviating this information overload, providing tailored Web experiences. This chapter explores the different faces of personalization, traces back its roots and follows its progress. It describes the modules typically comprising a personalization process, demonstrates its close relation to Web mining, depicts the technical issues that arise, recommends solutions when possible, and discusses the effectiveness of personalization and the related concerns. Moreover, the chapter illustrates current trends in the field suggesting directions that may lead to new scientific results.

Web Personalization for E-Marketing Intelligence

2005 ◽  
pp. 48-68 ◽  
Author(s):  
Penelope Markellou ◽  
Maria Rigou ◽  
Spiros Sirmakessis
Keyword(s):  
Web Mining ◽  
Information Overload ◽  
User Interaction ◽  
Close Relation ◽  
Online Marketing ◽  
Web Personalization ◽  
Negative Effects ◽  
Comprehensive Overview ◽  
Research Issues ◽  
The Web

As the Web is growing exponentially, online marketing has been changed by the newly provided technological capacities and digital channels of sales. Online marketing or e-marketing is the adaptation and development of marketing strategies in the Web environment and includes all factors that affect a Web site’s efficiency, like the idea, the content, the structure, the interface, the implementation, the maintenance, the promotion and the advertising. Since more and more businesses are using the Web to conduct their activities, issues like interface usability, easy navigation and effective supporting services become critical and influence their success dramatically. However, one important problem that arises is that Web users are confronted with too many options. Currently, Web personalization is the most promising approach to alleviate this information overload and to provide users with tailored experiences. It improves user interaction with Web sites and offers them the ability to establish long-term and loyal relationships. The scope of this chapter is to give a comprehensive overview of research issues on personalized e-marketing applications. We focus on the importance of personalization as a remedy for the negative effects of the traditional “one-size-fits-all” approach. Next, we explore the different steps of the personalization process providing information about interesting research initiatives and representative commercial tools for producing personalized Web experiences. Finally, we demonstrate the close relation between personalization and Web mining and discuss open research issues.

Web Personalization for E-Marketing Intelligence

2009 ◽  
pp. 2164-2180 ◽  
Author(s):  
Penelope Markellou ◽  
Maria Rigou ◽  
Spiros Sirmakessis
Keyword(s):  
Web Mining ◽  
Information Overload ◽  
User Interaction ◽  
Close Relation ◽  
Online Marketing ◽  
Web Personalization ◽  
Negative Effects ◽  
Comprehensive Overview ◽  
Research Issues ◽  
The Web

As the Web is growing exponentially, online marketing has been changed by the newly provided technological capacities and digital channels of sales. Online marketing or e-marketing is the adaptation and development of marketing strategies in the Web environment and includes all factors that affect a Web site’s efficiency, like the idea, the content, the structure, the interface, the implementation, the maintenance, the promotion and the advertising. Since more and more businesses are using the Web to conduct their activities, issues like interface usability, easy navigation and effective supporting services become critical and influence their success dramatically. However, one important problem that arises is that Web users are confronted with too many options. Currently, Web personalization is the most promising approach to alleviate this information overload and to provide users with tailored experiences. It improves user interaction with Web sites and offers them the ability to establish long-term and loyal relationships. The scope of this chapter is to give a comprehensive overview of research issues on personalized e-marketing applications. We focus on the importance of personalization as a remedy for the negative effects of the traditional “one-size-fits-all” approach. Next, we explore the different steps of the personalization process providing information about interesting research initiatives and representative commercial tools for producing personalized Web experiences. Finally, we demonstrate the close relation between personalization and Web mining and discuss open research issues.

Research on the Web Mining Algorithm Application in Tourism E-Commerce

2013 ◽  
Vol 380-384 ◽  
pp. 1133-1136
Author(s):  
Xue Song Zhao ◽  
Kai Fan Ji
Keyword(s):  
Association Rules ◽  
Clustering Analysis ◽  
Web Mining ◽  
Web Design ◽  
Developed Countries ◽  
Online Sales ◽  
Mining Algorithm ◽  
Personalized Services ◽  
Mining Algorithms ◽  
The Web

Web mining algorithms are widely used in e-commerce. Tourism e-commerce develops fast in recent years in China but the application of web mining algorithms stays in low level compared with some developed countries. This paper first discusses two major web mining algorithms: the Association Rules algorithm and Clustering Analysis, and then analyzes the application of web mining algorithm in tourism e-commerce. It concludes that web mining algorithms can help tourism e-commerce to improve web design, increase online sales and provide better personalized services for web users.

Protecting Datasources Over the Web

2003 ◽  
pp. 299-330
Author(s):  
Silvana Castano ◽  
Eena Ferrari
Keyword(s):  
Access Control ◽  
Relevant Information ◽  
Security Policies ◽  
Web Based ◽  
Information Release ◽  
Private And Public ◽  
Definition Of ◽  
Access To Data ◽  
Acquisition Processes ◽  
The Web

Since the Web is becoming the main means of disseminating information in private and public organizations, both at internal and external levels, several applications at Internet and intranet level need mechanisms supporting a selective access to data available over the Web. Through XML, the document exchange and acquisition processes, which can be very frequent in Web-based systems, are simplified and standardized. The development of suitable security policies for both access control and information release and distribution are relevant research topics in the security field, and XML compatibility is an important requirement for Web datasource protection. This chapter covers the issues related to the definition of security policies, models and mechanisms for access control and dissemination of Web data, and is organized in two parts. In the first part, we introduce the general issues and requirements related to the definition of different types of security policies for access control and for information release in Web datasources. Then, we present security policies and mechanisms specifically devoted to the protection of XML data. In the second part, we describe the use of XML for the specification of security relevant information, focusing on security policies, subject credentials, and content protection.

Mining Association Rules from XML Documents

2011 ◽  
pp. 879-899
Author(s):  
Laura Irina Rusu ◽  
Wenny Rahayu ◽  
David Taniar
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Web Application ◽  
Semistructured Data ◽  
Markup Language ◽  
Xml Documents ◽  
Rapid Changes ◽  
Extensible Markup ◽  
Hidden Knowledge ◽  
The Web

This chapter presents some of the existing mining techniques for extracting association rules out of XML documents in the context of rapid changes in the Web knowledge discovery area. The initiative of this study was driven by the fast emergence of XML (eXtensible Markup Language) as a standard language for representing semistructured data and as a new standard of exchanging information between different applications. The data exchanged as XML documents become richer and richer every day, so the necessity to not only store these large volumes of XML data for later use, but to mine them as well to discover interesting information has became obvious. The hidden knowledge can be used in various ways, for example, to decide on a business issue or to make predictions about future e-customer behaviour in a Web application. One type of knowledge that can be discovered in a collection of XML documents relates to association rules between parts of the document, and this chapter presents some of the top techniques for extracting them.

Web Mining for Public E-Services Personalization

2010 ◽  
pp. 751-758
Author(s):  
P. Markellou
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

scholarly journals Improved PageRank Algorithm for Web Structure Mining

2013 ◽  
Vol 10 (9) ◽  
pp. 1969-1976
Author(s):  
Sathya Bama ◽  
M.S.Irfan Ahmed ◽  
A. Saravanan
Keyword(s):  
Search Engines ◽  
Web Mining ◽  
Web Search ◽  
Relevant Information ◽  
Research Area ◽  
Pagerank Algorithm ◽  
Web Structure ◽  
User Query ◽  
Web Structure Mining ◽  
The Web

The growth of internet is increasing continuously by which the need for improving the quality of services has been increased. Web mining is a research area which applies data mining techniques to address all this need. With billions of pages on the web it is very intricate task for the search engines to provide the relevant information to the users. Web structure mining plays a vital role by ranking the web pages based on user query which is the most essential attempt of the web search engines. PageRank, Weighted PageRank and HITS are the commonly used algorithm in web structure mining for ranking the web page. But all these algorithms treat all links equally when distributing initial rank scores. In this paper, an improved page rank algorithm is introduced. The result shows that the algorithm has better performance over PageRank algorithm.

scholarly journals A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining

2021 ◽  
Vol 12 (2) ◽  
pp. 1634-1639
Author(s):  
V Aruna, Et. al.
Keyword(s):  
Web Mining ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Relevant Information ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Web Usage ◽  
Hidden Data ◽  
The Web

In the recent years with the advancement in technology, a  lot of information is available in different formats and extracting the  knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem  Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one  of the subset of  Web Mining helps in extracting the hidden knowledge present in the Web log  files , in recognizing various interests of web users and also in  discovering customer behaviours. Web Usage mining  includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining  algorithms  like  apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy,  sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison  is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

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