Usage mining

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Web Usage Mining for Predicting Users’ Browsing Behaviors by using FPCM Clustering

Web Usage Mining for Predicting Users’ Browsing Behaviors by using FPCM Clustering

Forecasting the user’s browsing pattern is a significant technique for many applications. The Forecasting results can be utilized for personalization, building proper web site, enhancing marketing strategy, promotion, product supply, getting marketing data, forecasting market trends, and enhancing the competitive strength of enterprises etc. This paper uses web usage mining technique for predicting the user’s browsing behavior. One of the effective existing techniques for web usage mining is the usage of hierarchical agglomerative clustering to cluster users’ browsing behaviors. The usage of Two Levels of Prediction Model framework is explained in this paper which works better for general cases. However, Two Levels of Prediction Model suffer from the heterogeneity user’s behavior. To overcome this difficulty, this paper uses Fuzzy Possibilistic algorithm for clustering. The experimental result shows that the proposed technique results in higher hit rate.
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Enhancing Web Navigation Usability Using Web Usage Mining Techniques

Enhancing Web Navigation Usability Using Web Usage Mining Techniques

information resources and services. That’s why there is an explosive growth in web traffic. Each and every website has some form of navigation which is decided by the web developer. And most of the time we knew that these navigational paths are decided without looking into users web interest, which results into the navigational problems have to face by user. This paper “Enhancing web navigation usability by using web usage mining techniques” discusses in detail and provides a standard way for web developers to recognize actual usage behavior and anticipated usage behavior with the use of server side access log record file. Along with this it discusses and provides facility for updating web links in an automated manner based on sequential pattern mining and thereafter pattern analysis results. Anticipated Usage behavior helpful for user to provide better effectiveness and efficiency for their tasks as well as updating links in an automated manner reduces the time expended by developer. Overall this system is more useful from web developers’ as well as users’ point of view.
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A Survey on Methods used in Web Usage Mining

A Survey on Methods used in Web Usage Mining

With the increasing demand of internet more number of websites is being involved for getting required information and thus more usage of web-based data. The data which is stored in different format types in the web log file. This log file should be maintained as these data are in unsorted manner and it is done through preprocessing. Web usage mining focuses on discovering useful information. Web log file is automatically generated by web server whenever user accesses the resource like webpage of website.

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Model Survey on Web Usage Mining and Web Log Mining

Model Survey on Web Usage Mining and Web Log Mining

Abstract - At present in our day to day life internet plays a very important role. It has become a very vital part of human life. As internet is growing day by day, so the users are also expanding at much greater rate. Users spend lot of time on internet depending on the behavior of different user. Internet provides huge amount of information and from this information knowledge is extracted for the users. This extraction of information demands for the new logics and method. The data mining techniques and applications can be used in web based applications for performing this job which is also known as web mining. Web based mining or web usage mining is one of the trending topics nowadays. When user uses internet or visits some web pages, the associated information are stored in the server log files. Using these log files of server the human nature or behavior can be predicted. This paper focus on the web based mining and how it can be can be used to predict the human behavior using the server log files. The paper contains some of the techniques and methods associated with web mining.
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An Effective Web Usage Mining

An Effective Web Usage Mining

Abstract — Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from web data, specifically web logs, in order to improve web based applications. Web usage mining consists of three find phases–, pre-processing, pattern discovery, and pattern analysis. After completion of these three phases the user can the required usage patterns and use these information for specific needs. Web log files are the primary data source for web usage mining. This usage analysis includes tasks like page access frequency, finding the common traversal paths through a website. These log files contains information that can’t be directly interpreted, for example information like who is accessing, which pages are accessing by whom, how much time user is accessing a particular page ,can’t be obtained directly these log files. Since log files are unformatted text files, they are complex to interpret and analyze. In this paper we propose a novel approach using universally accepted formatting language XML. In our approach text based log files are converted into XML format using parsers. Once a log file is in XML format, using DOM API or types of parser API’s we can retrieve the required information in an easy manner such as user and session identification and the paths that are frequently accessed. This paper presents several data preparation techniques based on XML parsers in order to increase the usability of websites.
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Web Usage Mining: A Survey

Web Usage Mining: A Survey

Now-a-days internet has become an essential part of our daily life. In the past decade, there is an outstanding growth in number of websites & visitors. Because of this, large amount of data has been generated [5]. Data mining involves analysis of data sets to find unsuspected relationships and to summarize the data. Web mining is one of the most noteworthy fields in the area of data mining. Web Mining is the application of data mining techniques to Web data, which can be Web document content or hyperlink structure or Web log file, to discover and mine the undiscovered knowledge and useful patterns. Web mining is divided in the following three categories: Web content mining is the extraction, mining and integration of useful data, information and knowledge from Web page content. Web content mining is differentiated from two different points of view: Information Retrieval View and Database View. Web structure mining focuses on the underlying structure of the websites. It can be used to categorize web pages and is useful to generate information such as the similarity and relationship between different web sites. Web usage mining is the process of finding out what users are
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Novel Web Usage Mining for Web Mining Techniques

Novel Web Usage Mining for Web Mining Techniques

In this paper, a new session reconstruction algorithm is introduced. This algorithm is better than previously developed both time and navigation oriented heuristics as it does not allow page sequences with any unrelated (without any hyperlinks from the preceding page to the next page) consecutive requests to be in the same session. Navigation oriented heuristics insert artificial browser (back) requests into a session in order to guarantee that consecutive requests will have connectivity between each other. Thus, the session sequences are shorter and easier to process than those generated by navigation oriented heuristics. This algorithm also enhances navigation-oriented heuristics by using a time oriented extension restricting requests in a session to be at most within a 30 minute period. Another advantage of our heuristic is that it guarantees that all sessions generated will be maximal sequences and do not subsume any other session. We have implemented agent simulator for generating real user sessions. Our agent simulator generates real sessions satisfying both connectivity and timestamp rules. We have compared the sessions reconstructed by our heuristic and previous heuristics against the real sessions generated by the agent simulator. We have also defined the real accuracy of the constructed sessions as a sequence and subsequence relationship. As a result, our approach seems a reasonable method for using reactive web usage mining in real world applications.
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Web Usage Mining and Business Intelligence

Web Usage Mining and Business Intelligence

Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the World Wide Web. Web usage mining provides the support for the web site design, providing personalization server and other business making decision, etc. In order to better serve for the users, 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. It has quickly become one of the most important areas in Computer and Information
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A Survey of Issues and Techniques of Web Usage Mining

A Survey of Issues and Techniques of Web Usage Mining

role in field of computer science. The World Wide Web is an interactive and popular platform to transfer information. Web Usage Mining is the type of web mining and it is application of data mining techniques. Web Usage Mining has become helpful for website management, personalisation etc. Usage data internment the origin of web users along with their browsing behaviour at a website. It means weblog records to discover user access pattern from web pages. Weblog contains all information regarding to users which is useful to access pattern. Web mining helps to gather the information from customer who’s visiting the site. Now a days various issues related to log files i.e. data cleaning, session identification, user identification etc. In this survey paper we discuss the phases of WUM, architecture of WUM, issues related to WUM and also discuss the future direction.
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A Study on Association Rule Mining Algorithms Used in Web Usage Mining

A Study on Association Rule Mining Algorithms Used in Web Usage Mining

ABSTRACT: Web Usage Mining is an application of Data Mining which is used to identify the user needs from web log. It does so by discovering interesting and most frequent patterns based on users’ navigational behaviors. Source data mainly consist of the logs that are collected when users access web servers and might be represented in standard format. Web server log files act as storage for frequent word sequences. The word sequence comprises of IP address, page reference and access time. The study focuses on comparison of Apriori, AprioriTID and AprioriHybrid algorithms.
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Clustering with Efficient Web Usage Mining

Clustering with Efficient Web Usage Mining

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.
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A Survey on Web Usage Mining Preprocessing

A Survey on Web Usage Mining Preprocessing

Web usage mining is the application of data mining techniques for discovering interesting usage patterns from web usage data, in order to understand and better serve the needs of web-based applications. Usage data captures the identity and origin of web users along with their browsing behavior at a web site. Web usage mining tries to make sense of data generated by the web surfer‟s session or behaviour . Web usage mining itself can be classified further depending on the kind of usage data considered. First one is Web Server Data in which user logs are collected by the web server and typically include IP address, page reference and access time. Second is Application Server Data which track various kinds of business events and log them in application server logs. And third one is Application Level Data in which new kinds of events can be defined in an application, and logging can be turned on for them - generating histories of these specially defined events.
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An Efficient Clustering Technique For Weblogs

An Efficient Clustering Technique For Weblogs

ABSTRACT- Web mining research includes several research communities such as database, information recovery and artificial intelligence. Web mining divided into three categories they are web usage mining, web content mining and web structure mining. Web content mining is used to extract useful information from the webpages. Structure mining deals with in link and out link and web usage mining is to discover interesting usage patterns from the web data. The pre-processing step can improve text quality by eliminating irrelevant data. Clustering is to group the data based on their similarities. This paper mainly focuses on clustering web log data to identify user access pattern. In first phase going to pre-process the web logs data. Pre-processing step is done to make the sample raw web logs more efficient. If the web logs are large in size with unwanted data it will not provide better result. So, Pre-processing step is done. In second phase clustering techniques is used. Sample Web logs are clustered using k-means clustering and farthest first clustering technique. By clustering the webpages we can easily identify the user interest and the access pattern. In third phase visualize the clustered same weblogs.
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Volume 2, Issue 7, July 2013 Page 155

Volume 2, Issue 7, July 2013 Page 155

Web content usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks which helps to investigate the node and connection structure of web sites. According the type of web structural data, web structure mining can be divided into two kinds 1)extracting the documents from hyperlinks in the web 2) analysis of the tree-like structure of page structure. Based on the topology of the hyperlinks, web structure mining will categorize the web page and generate the information, such as the similarity and mining is concerned with the retrieval of
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The Probability of Predicting E-Customer's Buying Pattern Based on Personality Type

The Probability of Predicting E-Customer's Buying Pattern Based on Personality Type

The term ‘data mining’ is used to describe the process of analysing a company’s internal data for customer profiling and targeting. In e- commerce application, the end goal of data mining is to improve processes that contribute to deliver value to the end customer (Jiang & Yu, 2008). Mining is an umbrella term which includes disparate areas of study. In this paper, in order to present how different areas try to predict customer behaviour pattern, we are going to briefly review Data Mining (DM), Web Mining (WB) and Web Usage Mining (WUM).
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Analysis of Web Usage Behavior using Pattern Analyzing Techniques in  E-Learning system

Analysis of Web Usage Behavior using Pattern Analyzing Techniques in E-Learning system

since in a medium size site log files amount to several megabytes a day, there is a necessity of techniques and tools to help take advantage of their content. Application Level Data is another source for web usage mining. With this type of data it is possible to record various kinds of events in an application. These data is used for generating histories about selected special events [3]. The data in this category can be divided into three categories based on the source of its collection: on the server side, the client side, and the proxy side.
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Review of Web Recommendation System and Its
          Techniques: Future Road Map

Review of Web Recommendation System and Its Techniques: Future Road Map

The concept of web usage mining is playing main role for identifying the web page requirements of end users through the web server. Generally the end users want to find the right web pages within the short duration of time. So the need of demand, the development is required to forecast the correct web pages from the web. Many techniques applied to the analysis of web log data, but researchers have been attracted by ARM. Preprocessing is for Web Usage Mining works basis. Preprocessing methods discussed the importance of this work; various techniques are compared and identified. Preprocessing techniques to preprocess a complete extraction of user patterns, web log files are proposed [1]. Data cleaning algorithms irrelevant web log files and remove entries from the log file filtering algorithm discards unselfish characteristics. Users are able to identify the session. Sanjay Gandhi et al also a full stream of data preprocessing techniques proposed for use. The preprocessing stage and search log data is collected from different data sources are used before meaningful patterns. Web mining valuable information from secondary data derived from user access logs. It is important for web site organization, improve business services, personalization web traffic and web recommendation. Web usage mining divided into three different phases and these are planned. Big web traffic data calculated & applied to web mining techniques for discovering an interesting pattern useful from traffic analysis.
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Page Ranking Algorithm Based on Counts of Link
          Hits (PRCLH): An Implementation

Page Ranking Algorithm Based on Counts of Link Hits (PRCLH): An Implementation

Web Usage Mining (WUM) tries to discover user navigation patterns from web data and the useful information from the secondary data derived from the interactions of the users while surfing on the Web. It focuses on the techniques that could predict user behavior while the user interacts with Web. This type of web mining allows for the collection of Web access information for Web pages. This usage data provides the paths leading to accessed Web pages. This information is often gathered automatically into access logs via the Web server. CGI scripts offer other useful information such as referrer logs, user subscription information and survey logs. This category is important to
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UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES

UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES

Web Usage Mining is a computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis and database systems with the goal to extract valuable information from accessing server logs of World Wide Web data repositories and transform it into an understandable structure for further understanding and use. Main focus of this paper will be centered on exploring methods that expedites the log mining process and present the result of log mining process through data visualization and compare data-mining algorithms. For the comparison between classification techniques, precision, recall and ROC area are the correct measures that are used to compare algorithms. Based on this study it shows that Naïve Bayes and Bayes Network are proven to be the best algorithms for that.
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Web Log Based Analysis of User’s Browsing Behavior Ashwini ladekar Pooja Pawar

Web Log Based Analysis of User’s Browsing Behavior Ashwini ladekar Pooja Pawar

Estimating the interest of a user as he/she visits Web pages has gained an importance as Web-based activities have increased. With the tremenduous growth of World Wide Web, the study of modeling and predicting a user's access on a Web site has become more important. Web usage mining is an application of data mining technique to discover usage patterns from Web data. It helps to understand and serve the need of user. The discovered patterns are usually represented as collections of pages, objects, or resources that are frequently accessed by groups of users with common needs or interests.
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