IDENTIFY NAVIGATIONAL PATTERNS OF WEB USERS

2013

Web server log repositories are great source of knowledge, which keeps the record of web usage patterns of different web users. The Web usage patt ern analysis is the process of identifying browsing patterns by analyzing the user's navigational behav ior. World Wide Web is a large amount of data provi der and a very large source of information. Users are increasing day by day for accessing web sites. For effective and efficient handling, web mining coupled with suggestion techniques provides personalized contents a t the disposal of users. Web Mining is an area of Data Mining dealing with the extraction of interesting kno wledge from the Web. Here we are presenting a personalization process based on Web usage mining. This paper reviews the process of discovering useful patterns from the web server log file. In this a host of Web usage mining activities required for this process, includ ing the pre-processing and integration of data from multiple sources, and common pattern disco...

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013

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. Web usage mining is a kind of data mining method that provide intelligent personalized online services such as web recommendations, it is usually necessary to model users" web access behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use this information for the specific needs. The data abstraction is achieved through data preprocessing. The aim of discovering frequent sequential access patterns in Web log data is to obtain information about the navigational behavior of the users. In the proposed system, an efficient sequential pattern mining algorithm is used to identify frequent sequential web access patterns. The access patterns are retrieved from a Graph, which is then used for matching and generating web links for recommendations.

2017

An enormous amount of data is available in form of web documents over the World Wide Web and is increasing day by day. Web mining is used to extract useful information from web documents. Web mining is categorized into three types, namely, web content mining, web structure mining and web usage mining. Web usage mining is the data mining technique to mine the web log data from World Wide Web to extract useful information. Web usage mining useful for the applications like ecommerce to do personalized marketing, fight against terrorism, fraud detection, to identify criminal activities, web design etc. This paper is going to explain in detail about the process involved in Web Usage Mining, Web Usage Mining applications and tools. Keywords-Web Usage Mining, Web log files, Web usage mining process, Web usage mining applications and Tools.

2015

In today's era, the internet playing an essential role in our day-today life. The internet has influenced every area of users. The tremendous growth of an internet raises the complexity to browse efficiently by the users. To understand and increase the performance of the website, a website is changed as per user's needs. Web log files analyses to fulfil the needs of auser; these log files are stored on server side. Through which the behaviour of users is capture. Web usage mining is an application to analyse interesting usage patterns from web log data. Web usage mining process is to discover patterns in three phase processes: data pre-processing, pattern discovery, pattern analysis. Log data contain an impurity and noise that why this data cannot directly used for pattern discovery. Pre-processing step is important in the mining process to get a right pattern from log data. Data pre-processing includes three steps data cleaning, user identification and session identificatio...

Abstract In recent years the growth of the World Wide Web exceeded all expectations. Today, there are several billions of HTML documents, Pictures and other multimedia files available via Internet and the number is still rising. In real problems, several studies have been conducted, presented and used on user behaviour in Internet. Various studies on Internet user behaviours have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing user behaviour. This paper is a survey of recent work in the field of web usage mining for the benefit of research on the Internet user behaviour. A main research area in Web mi ning focused on learning Web users and their interactions within Websites is Web usage mining. Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. A lot of research has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. Keywords : Web Usage Mining, Web log file, User behaviour, Appl ication WUM .

2013

The process to discover and extract useful information is web usage mining. It helps to better understand and achieve the needs of web-based applications. To enhance the efficiency and to ease of the mining process, the data should be preprocessed. The proper study and analysis of web log file is useful to manage the websites effectively for administrative and user’s perspective. Many a time the backward referencing used to track the reachability of the pages which consumes time and generates complete path from the root node even if the link has come from some other server page. This problem can be using two-way hash table structure as Access History List. In our system, session identification is done using AHL by considering immediate link analysis, backward referencing without searching the whole tree representing the server pages. Based on this study, it can be concluded that the system is complex but user session sequences are generated with less time and greater precision.

Proceedings of the 18th International Conference on World Wide Web, 2009

In this paper, we propose a novel framework called Smart-Miner for web usage mining problem which uses link information for producing accurate user sessions and frequent navigation patterns. Unlike the simple session concepts in the time and navigation based approaches, where sessions are sequences of web pages requested from the server or viewed in the browser, Smart Miner sessions are set of paths traversed in the web graph that corresponds to users' navigations among web pages. We have modeled session construction as a new graph problem and utilized a new algorithm, Smart-SRA, to solve this problem efficiently. For the pattern discovery phase, we have developed an efficient version of the Apriori-All technique which uses the structure of web graph to increase the performance. From the experiments that we have performed on both real and simulated data, we have observed that Smart-Miner produces at least 30% more accurate web usage patterns than other approaches including previous session construction methods. We have also studied the effect of having the referrer information in the web server logs to show that different versions of Smart-SRA produce similar results. Our another contribution is that we have implemented distributed version of the Smart Miner framework by employing Map/Reduce Paradigm. We conclude that we can efficiently process terabytes of web server logs belonging to multiple web sites by our scalable framework.

Continued growth of user number and size of shared content on Web sites cause the necessity of automatic adjusting content to users' needs. In the literature of Web Mining, such actions are referred to personalization and recommendation which led to improve the visibility of presented content. To perform adequacy actions which correspond to the expected users' needs we can utilize web server log files. Mining such data with accurate constraints can lead to the discovery of web user navigation patterns. Such knowledge is used by personalization and recommendation systems (PRS) due to performed actions against user behavior during a visit on the web portal. In these paper we present the system framework for mining web user navigation patterns in order to knowledge management. We focus on constraints which are critical factors to evaluate the effectiveness of the implemented algorithm. On the other hand, these constraints can be perceived as knowledge validation criteria due to its adequacy. Thus only adequate knowledge can be added to existing in PRS knowledge base.

Arxiv preprint arXiv:0804.1409, 2008

Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web users. The first phase of web usage mining is the data processing phase, which includes the session reconstruction operation from server logs. Session reconstruction success directly affects the quality of the frequent patterns discovered in the next phase. In reactive web usage mining techniques, the source data is web server logs and the topology of the web pages served by the web server domain. Other kinds of information collected during the interactive browsing of web site by user, such as cookies or web logs containing similar information, are not used. The next phase of web usage mining is discovering frequent user navigation patterns. In this phase, pattern discovery methods are applied on the reconstructed sessions obtained in the first phase in order to discover frequent user patterns. In this paper, we propose a frequent web usage pattern discovery method that can be applied after session reconstruction phase. In order to compare accuracy performance of session reconstruction phase and pattern discovery phase, we have used an agent simulator, which models behavior of web users and generates web user navigation as well as the log data kept by the web server.

2013

Abstract-As there is a huge amount of data available online, the World Wide Web is a fertile area for data mining research. In recent years a various surveys have been performed on static data of web sites to perform web usage mining. This paper deals with the Web usage mining of a website which is hosted on IIS web server. 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 perform improvements in web based applications. Web usage mining consists of three phases, pre-processing, pattern discovery, and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use this information for the specific needs. The research is being performed on a log file using Log Parser.