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A Web Usage Mining Framework for Business Intelligence
Sonal Tiwari
International Journal of Electronics Communication and Computer Technology , 2011,
Abstract: In this paper, we introduce a web mining solution to business intelligence to discover hidden patterns and business strategies from their customer and web data. We propose a new framework based on web mining technology. Web mining attempts to determine useful knowledge from secondary data obtained from the interactions of the users with the web.
Business Intelligence: A Rapidly Growing Option through Web Mining  [PDF]
Priyanka Rahi
Computer Science , 2012,
Abstract: The World Wide Web is a popular and interactive medium to distribute information in this scenario. The web is huge, diverse, ever changing, widely disseminated global information service center. We are familiar with terms like e-commerce, e-governance, e-market, e-finance, e-learning, e-banking etc. for an organization it is new challenge to maintain direct contact with customers because of the rapid growth in e-commerce, e-publishing and electronic service delivery. To deal with this there is need of intelligent marketing strategies and CRM (customer relationship management) i.e. the effective way of integrating enterprise applications in real time. Web mining is the vast field that helps to understand various concepts of different fields. Web usage mining techniques are attempted to reason about different materialized issues of Business Intelligence which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes. To this end, the chapter provides an introduction to the field of Web mining and examines existing as well as potential Web mining applications applicable for different business function, like marketing, human resources, and fiscal administration. Suggestions for improving information technology infrastructure are made, which can help businesses interested in Web mining hit the ground running.
Fuzzy Clustering: An Approachfor Mining Usage Profilesfrom Web  [PDF]
Ms.Archana N. Boob,Prof. D. M. Dakhane
International Journal of Computer Technology and Applications , 2012,
Abstract: Web usage mining is an application of data mining technology to mining the data of the web server log file. It can discover the browsing patterns of user and some kind of correlations between the web pages. Web usage mining provides the support for the web site design, providing personalization server and other business making decision, etc. Web mining applies the data mining, the artificial intelligence and the chart technology and so on to the web data and traces users' visiting characteristics, and then extracts the users' using pattern.In this paper, we present an approach to cluster Web site users into different groups. By using fuzzy clustering, we enable generation of overlapping clusters that can capture the uncertainty among Web user’s navigation behaviour.
Data, Text and Web Mining for Business Intelligence : A Survey
Abdul-Aziz Rashid Al-Azmi
International Journal of Data Mining & Knowledge Management Process , 2013,
Abstract: The Information and Communication Technologies revolution brought a digital world with huge amountsof data available. Enterprises use mining technologies to search vast amounts of data for vital insight andknowledge. Mining tools such as data mining, text mining, and web mining are used to find hiddenknowledge in large databases or the Internet. Mining tools are automated software tools used to achievebusiness intelligence by finding hidden relations,and predicting future events from vast amounts of data.This uncovered knowledge helps in gaining completive advantages, better customers’ relationships, andeven fraud detection. In this survey, we’ll describe how these techniques work, how they are implemented.Furthermore, we shall discuss how business intelligence is achieved using these mining tools. Then lookinto some case studies of success stories using mining tools. Finally, we shall demonstrate some of the mainchallenges to the mining technologies that limit their potential.
Analysis of Server Log by Web Usage Mining for Website Improvement  [PDF]
Navin Kumar Tyagi,A. K. Solanki,Manoj Wadhwa
International Journal of Computer Science Issues , 2010,
Abstract: Web server logs stores click stream data which can be useful for mining purposes. The data is stored as a result of user's access to a website. Web usage mining an application of data mining can be used to discover user access patterns from weblog data. The obtained results are used in different applications like, site modifications, business intelligence, system improvement and personalization. In this study, we have analyzed the log files of smart sync software web server to get information about visitors; top errors which can be utilized by system administrator and web designer to increase the effectiveness of the web site.
Implementation of E-Service Intelligence in the Field of Web Mining
PROF. MS. S. P. SHINDE,,PROF. V.P.DESHMUKH
International Journal of Engineering Science and Technology , 2011,
Abstract: The World Wide Web is a popular and interactive medium to disseminate information today .The web is huge, diverse, dynamic, widely distributed global information service centre. We are familiar with the terms like e-commerce, e-governance, e-market, e-finance, e-learning, e-banking etc. These terms come under online services called e-service applications. E-services involve various types of delivery systems, advanced information technologies, methodologies and applications of online services. The keyword intelligence will be the next paradigm shift in the e-services, thanks to internet technological advances. Intelligence is closely related with Artificial Intelligence. Web Mining is the technique used to crawlthrough various web resources to collect required information, which enables an individual to promote business, understanding marketing dynamics, and new promotions floating on the Internet etc. Thetaxonomy of web mining can be broadly divided into three distinct categories; according to the kinds of data to be mined they are Web Content Mining, Web Structure Mining and Web Usage Mining. Thereare a lot of techniques of web mining however, artificial intelligence techniques and algorithms are being used by almost all web mining tasks for their efficiency. This paper discusses the two main AI techniques; the Multi-Agent Systems and Swarm Intelligence, with some of their applications in web mining. Web Mining Intelligent techniques can be combined with traditional web mining approaches to improve the quality of mining.
Data, text and web mining for business intelligence: a survey  [PDF]
Abdul-Aziz Rashid Al-Azmi
Computer Science , 2013, DOI: 10.5121/ijdkp.2013.3201
Abstract: The Information and Communication Technologies revolution brought a digital world with huge amounts of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden knowledge in large databases or the Internet.
Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming  [PDF]
Ajith Abraham,Vitorino Ramos
Computer Science , 2004,
Abstract: The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach. KEYWORDS: Web Usage Mining, Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming.
Efficient Web Usage Mining With Clustering
K. Poongothai,M. Parimala,S. Sathiyabama
International Journal of Computer Science Issues , 2011,
Abstract: Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis etc., Web site under study is part of a nonprofit organization that does not sell any products. It was crucial to understand who the users were, what they looked at, and how their interests changed with time. To achieve this, one of the promising approaches is web usage mining, which mines web logs for user models and recommendations. Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this study we advance a model for mining of user's navigation pattern. The proposal of our work proceeds in the direction of building a robust web usage knowledge discovery system, which extracts the web user profiles at the web server, application server and core application level. The proposal optimizes the usage mining framework with fuzzy C means clustering algorithm (to discover web data clusters) and compare with Expected Maximization cluster system to analyze the Web site visitor trends. The evolutionary clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using inference system. By linking the Web logs with cookies and forms, it is further possible to analyze the visitor behavior and profiles which could help an e-commerce site to address several business questions. Experimentation conducted with CFuzzy means and Expected Maximization clusters in Syskill Webert data set from UCI, shows that EM shows 5% to 8% better performance than CFuzzy means in terms of cluster number.
The Role of Web Usage Mining in Web Applications Evaluation  [PDF]
Sa?a Bo?njak,Mirjana Mari?,Zita Bo?njak
International Scientific Journal of Management Information Systems , 2010,
Abstract: The role of Web applications in corporate business has changed due to strong market competition and improved clients' negotiation power, imposing a new approach to their quality evaluation, in a sense that some analysis should be made prior to the implementation phase in order to reduce errors and inconsistencies in application design, while after the implementation, visiting scenarios and visitors' usage habits should be analyzed. The most common approach to the later task is to mine web access logs enriched by semantic information - web usage mining, in order to discover patterns hidden in data obtained through interaction of users on the web. In the paper we firstly provide a brief overview of data preprocessing, pattern discovery and pattern analysis steps of web mining and of most common pattern discovery methods. The remaining section demonstrates a practical example of Web site evaluation.
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