Top PDF Enhanced Web Mining Technique To Clean Web Log File

Enhanced Web Mining Technique To Clean Web Log File

Enhanced Web Mining Technique To Clean Web Log File

Web pages typically contain a large amount of information that is not part of the main content of the pages, e.g. banner ads, navigation bars, copyright notices, etc. Such noise on web pages usually leads to poor results in Web Mining which mainly depends upon the web page content. Therefore, it becomes very essential to extract information from the bulks of data and structure them into useful knowledge that will be helpful for some type of understanding. This leads to the birth of data mining. Web usage mining is the subject field of Data Mining which deals with the discovery and analysis of usage patterns from web data specifically web logs in order to improve the web based applications. The motive of mining is to find users’ access models automatically and quickly from the vast Web log data, such as frequent access paths, frequent access page groups and user clustering. Through web usage mining, the server log, registration information and other relative information left by user provide foundation for decision making of organizations.
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A Survey on Preprocessing of Web Log File in Web Usage Mining to Improve the Quality of Data

A Survey on Preprocessing of Web Log File in Web Usage Mining to Improve the Quality of Data

According to Murata and Saito (2006) user’s interests can be revealed through graph based on their web surfing. In this research, first authors collected the users’ accesses from the user search keyword and graph is generated from it. By applying PageRank algorithm mined from graph to assign importance to accessed pages. In next step, unwanted nodes and weak edges were removed from graph. At last, graph is decomposed into further sub graphs, which depict the behaviour of users surfing. The usage of client log file in WUM has become ineffective so it is important to mention that some sort of cleaning was performed to remove the inconsistent and noisy data. User’s interests can be mined in better way by grouping the interests based on page visited in a particular time interval.
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A Comprehensive Survey on Data Preprocessing          Methods in Web Usage Minning

A Comprehensive Survey on Data Preprocessing Methods in Web Usage Minning

Abstract—Web usage mining is the application of data mining technique which is used to extract information about user’s interest from web server log files. Web usage mining is widely used by companies to analyze the customer’s interest and predict future of their business. It is used in various fields like E-Business, E-Commerce, E- learning, etc., Web usage mining entails of three phases :- Data Preprocessing , Pattern Discovery and Pattern analysis. Data Preprocessing is one of the essential and a preliminary step in web mining to enforce quality in the input data. The raw data from web server log file is preprocessed to eliminate the noisy, vague and redundant data for efficient mining. It involves different phases namely Field Extraction and Data cleaning, User Identification, Session Identification, Path completion and Transaction Identification. In this paper, we have discussed about various researches carried out in Data Cleaning and the various attributes considered in the process of cleaning.
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Online Full Text

Online Full Text

Abstract—Growth of data over time especially in term of volume, velocity, value, veracity and variety led to many challenges especially in extracting useful information from it. Furthermore, managing and transforming raw data into a readable format is crucial for subsequent analysis. Therefore, this paper presents a new web server log file classification and an efficient way of transforming raw web log files by using knowledge database discovery (KDD) technique into a readable format for data mining analysis. An experiment was conducted to the raw web log files, in a controlled lab environment, by using KDD technique and k-nearest neighbor (IBk) algorithm. Based on the experiment conducted, the IBk algorithm generates 99.66% for true positive rate (TPR) and 0.34% for false positive rate (FPR) which indicates the significant efficiency of the new web log file classification and data transformation technique used in this paper.
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AN EFFICIENT STRATEGY OF PREPROCESSING FOR OBTAINING KNOWLEDGE FROM WEB USAGE DATA

AN EFFICIENT STRATEGY OF PREPROCESSING FOR OBTAINING KNOWLEDGE FROM WEB USAGE DATA

The data preprocessing technique is the set of operations that extract the information from the available source of information which can be used for further steps of Web Usage Mining. The available Web Usage Data is usually consists of irrelevant data, inconsistent data, noise, etc. For example, the user requests for graphical page content and requests for any other file which might be included into a web page or even navigation sessions performed by robots or web spiders which need to be removed, hence this preprocessing strategy have been proposed to extract the required data from the Web Usage Data. Data preprocessing is predominantly significant phase in Web Usage Mining due to characteristics of web data and its association to other related data collected from the multiple source. The data provided by the data source can be used to construct a data model, the web server is the richest source of data, web serverwill collect large amountof data from web sites and these data is stored in the web log files.The web log files are act as input for this preprocessing. These log files are available in web servers.These log files contains the multi-user details in the log file format. The two main log file formats are Common Log Format (CLF) and Extended Common Log Format (ECLF).A typical line in CLF is shown in figure 2.Recently W3C [W3C log] has represented an improved format for Web server log known as Extended Common Log Format (ECLF), this ECLF log file consists of two more fields then the CLF, the referrer (the URL client was visiting before requesting the URL) and user agent (the software that claims to be using) fields. A typical line in ECLF is shown figure3. Both the CLF and ECLF consist of following fields: IP address: This represents the address of the client‘s host name. Rfc: It is the remote login name of the user but most of the time it‘s
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Title: REVIEW PAPER ON TO PERFORM PREDICATIONS USING DATA MINING & SESSION IDENTIFICATION

Title: REVIEW PAPER ON TO PERFORM PREDICATIONS USING DATA MINING & SESSION IDENTIFICATION

A web may allow users to interact and collaborate with each other in a social media dialogue as creators of user-generated context in a virtual community. So, World Wide Web becomes more popular and user friendly for transferring information. Therefore, people are more interested in analyzing log files which can offer more useful insight into web site usage. Web mining is one of the technique of data mining to extract useful information based on users' needs, under web mining, web usage mining is one of the application of data mining technology to extract information from weblog to analyze the user access to websites by. Web mining is the use of data mining technique to automatically discover and extract information from web documents and services.
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An Novel approach on Pre Processing Technique on Web log mining

An Novel approach on Pre Processing Technique on Web log mining

Web-based organizations in their daily operations have reached astronomical proportions. Log data is usually noisy and ambiguous and preprocessing is an important process for efficient mining process. In the preprocessing, first the data cleaning process includes removal of records of graphics, videos and the format information, the records with the failed HTTP status code, robots files, and Session and user identification is performed. The next primary goal is to learn the user’s navigation patterns and their use of web resources in web usage mining. Identifying the potential attributes and reducing the dimensionality of the data by not including irrelevant attributes are the major role of feature extraction. The assignment is to convert inconsistent length transactions into fixed-length feature vectors. The potential feature set extraction will lead to better understanding of the user navigation patterns in web server log files instead of taking into the consideration of whole instances in the log file.
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A SURVEY ON WEB LOG MINING AND PATTERN PREDICTION

A SURVEY ON WEB LOG MINING AND PATTERN PREDICTION

Web sites have abundant web usage log which provides great source of knowledge that can be used for discovery and analysis of user accessibility pattern. The web log mining is the process of identifying browsing patterns by analyzing the user’s navigational behaviour. The web log files which store the information about the visitors of web sites is used as input for web log mining and pattern prediction process. First these log files are pre-processed and converted into required formats so web usage mining techniques can apply on these web logs for frequent patterns. The obtained results can be used in different applications like modification of web sites, system improvement, business intelligence, and personalization etc.
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Web Log Analyzer for Semantic Web Mining

Web Log Analyzer for Semantic Web Mining

usage mining is data filtering and pre-processing. In that phase, Web log data should be cleaned or enhanced, and user, session and page view identification should be performed. Web personalization is a domain that has been recently gaining great momentum not only in the research area, where many research teams have addressed this problem from different perspectives, but also in the industrial area, where there exists a variety of tools and applications addressing one or more modules of the personalization process. Enterprises expect that by exploiting the information hidden in their Web server logs they could discover the interactions between their Web site visitors and the products offered through their Web site. Using such information, they can optimize their site in order to increase sales and ensure customer retention. Apart from Web usage mining, user profiling techniques are also employed in order to form a complete customer profile. Lately, there is an effort to incorporate Web content in the recommendation process, in order to enhance the
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Title: An Efficient Preprocessing Method to Detect User Access Patterns from Weblogs

Title: An Efficient Preprocessing Method to Detect User Access Patterns from Weblogs

The World Wide Web is a vast collection of web document such as text, images, and multimedia data too, which is not an easy task to retrieve the exact data from the web. Web mining technique is one of the data mining techniques, which is used to mine the data from the web. This paper described the data preprocessing of web usage mining from which the weblogs are cleaned effectively using the web log explorer tool. This tool has the flexible system of filters, which gives information about visitors who accessed the specific web page. It is used to remove the irrelative entries and generate useful log report. Now these preprocessed weblogs can be used for further stages of pattern discovery and pattern analysis of web usage mining.
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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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Pre-Processing: Procedure on Web Log File for Web Usage Mining

Pre-Processing: Procedure on Web Log File for Web Usage Mining

The purpose of data cleaning is to remove irrelevant items stored in the log files that may not be useful for analysis purposes. When a user accesses a HTML document, the embedded images, if any, are also automatically downloaded and stored in the server log. For example, log entries with file name suffixes such as gif, jpeg, GIF, JPEG, jpg and JPG can be removed. Since the main objective of data preprocessing is to obtain only the usage data, file requests that the user did not explicitly request can be eliminated. This can be done by checking the suffix of the URL name. In addition to this, erroneous files can be removed by checking the status of the request (such as status code 404). Data cleaning also involves the removal of references resulting from spider navigations which can be done by maintaining a list of spiders or through heuristic identification of spiders and Web robots. The cleaned log represents the user’s accesses to the Web site.
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Novel Pre Processing Technique for Web Log Mining by Removing Global Noise, Cookies and Web Robots

Novel Pre Processing Technique for Web Log Mining by Removing Global Noise, Cookies and Web Robots

Various commercial available web server log analysis tools are not designed for high traffic web servers and provide less relationship analysis of data relationships among accessed files which is essential to fully utilize the data gathered in the server logs [25]. Web server log file is a simple plain text file which record information about each user. Log file contain information about user name, IP address, date, time, bytes transferred, access request. A Web log is a file to which the Web server writes information each time a user requests a resource from that particular site. When user submit request to a web server that activity are recorded in web log file. Log file range 1KB to 100MB. Log file gives significant information to web server. Web server logs contain more information about visitor’s information in the access logs, usually in W3C format. There are also the error logs for each server that contains information on errors and problems that the server practiced. The statistical analysis introduces a set of parameters to describe user’s access behaviors. With those parameters it becomes easy for administrators to define concrete goals for organizing their web sites and improve the sites according to the goals. But the drawback in this analysis is that the results are independent from page to page. Since user’s behavior is expected to be different dependent on length of browsing time, the calculation of accurate browsing time is more important. The discovery of the users' navigational patterns using SOM is proposed by Etminani et al., [1]. Jianxi et al., [2] presented a Web usage mining technique based on fuzzy clustering in Identifying Target Group. Nina et al., [3] suggests a complete idea for the pattern discovery of Web usage mining. Wu et al., [4] given a Web Usage Mining technique based on the sequences of clicking patterns in a grid computing environment. The author discovers the usage of MSCP in a distributed grid computing surroundings and expresses its effectiveness by empirical cases. Aghabozorgi et al., [5] proposed the usage of incremental fuzzy clustering to Web Usage Mining. Rough set based feature selection for web usage mining is proposed by Inbarani et al., [7]. Jalali et al., [8] put forth a web usage mining technique based on LCS algorithm for online predicting recommendation systems. For providing the online prediction effectively, Shinde et al., [9] provides a architecture for online recommendation for predicting in Web
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Effective Cleaning of Educational Website Usage Patterns and Predicting their Next Visit

Effective Cleaning of Educational Website Usage Patterns and Predicting their Next Visit

Figure1: Categorization of Web Data mining Web content mining (WCM) is to find useful information in the content of web pages[4] e.g. free Semi-structured data such as HTML code, pictures, and various unloaded files. Web structure mining (WSM) is use to generating a structural summary about the web site and web pages. Web structure mining tries to discover the link structure of the hyperlinks at the inter document level. Web content mining mainly focuses on the structure of inner document, Web usage mining (WUM) is applied to the data generated by visits to a web site, especially those contained in web log files. I only highlighted and discussed research issues involved in web usage data mining. Web usage mining (WUM) or web log mining, users’ behavior or interests is revealed by applying data mining techniques on web. Three main sources of web log file are
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Index Terms— Hybrid agglomerative clustering, Soft Computing, Web sessionization, Web Usage Mining, Web Log Mining,

Index Terms— Hybrid agglomerative clustering, Soft Computing, Web sessionization, Web Usage Mining, Web Log Mining,

Clustering is one of the fundamental techniques to organize similar objects into proper groups based on features in the domain of data mining, machine learning and pattern recognition. In each cluster, objects are more similar to each other on the basis of particular features. Clustering has numerous applications in multiple domains such as information retrieval, data mining, machine learning, pattern recognition, mathematics, medical and bioinformatics. As a result of the unending extension of e-business, there is outstanding contention among relationship to pull in and hold customers. Examinations of the web server logs of these affiliations are essential for getting lots of information into web personalization lead, which can reinforce the arrangement of additionally engaging web structures. Web driven applications are growing step by step and the web has ended up one of the biggest information vaults. Similarity computation calculation among the information objects (web sessions) is mind boggling, however is a critical issue in unsupervised learning. This research is an attempt to overcome these challenges and problems. The objective of this research paper is to introduce a HAC based similarity measure to compute the similarity among the sessions. A HAC based approach is being applied to compute the statistically significant relationship between observed and expected frequencies of the number of pages visited and the time consumed by a user during a session. Also, Hierarchical agglomerative clustering (HAC) technique is proposed to extract useful knowledge from web log. This helps to improve the visualization of web logs and is equally important for website designers, developers and owners for the improvements of websites at each level. Experimental results with two different log files reveal that the proposed similarity measure with HAC algorithm has significantly improved the computation among data objects in web sessions.
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Web usage mining: Review on preprocessing of web log file

Web usage mining: Review on preprocessing of web log file

(a) Data cleaning: data cleaning focus on the getting rid of irrelevant & unimportant data from the web log server. A log file can provide useful information that helps a website engineer in enhancing the website structure in a way that will make the website usage easier and faster in future. This step consists of removing useless requests from the log files. Usually,this process removes requests concerning nonanalyzed resources such as images and multimedia files. Data cleaning also identifies Web robots and removes their requests.For Web portals and popular
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ABSTRACT: Web log file is log file automatically created and maintained by a web server.Analyzing web server

ABSTRACT: Web log file is log file automatically created and maintained by a web server.Analyzing web server

computationof MapReduce improves performance for large log files by breaking job into number of tasks. The Hadoopimplementation shows that MapReduce program structure can be effective solution for analyzing very large weblog files in Hadoop environment [9]. Hadoop-MR log file analysis tool that provides a statistical report on total hits of a web page, user activity, traffic sources was performed in two machines with three instances of Hadoop by distributing the log files evenly to all nodes [10].A generic log analyzer framework for different kinds of log fileswas implemented as a distributed query processing to minimize the response time for the users which can be extendable for some format of logs [11].Hadoopframework handles large amount of data in a cluster for web log mining. Data cleaning, the main part of preprocessing is performed to remove the inconsistent data. The preprocessed data is again manipulated using session identification algorithm to explore the user session. Unique identification of fields is carried out to track the user behavior [12].
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Web Page Noise Removal - A Survey

Web Page Noise Removal - A Survey

Removal of primary noises, duplicate content and noisy information is done according to the block importance of the web pages. The primary noise have copyright information, privacy notice, navigation bar, advertisements and etc. Block splitting technique is used to remove the primary noises. In block splitting is an important content of a web page which enclosed in div tag is considered. The main content is divided into a number of blocks. Simhash method is employed to remove the duplicate contents. Smash is a fingerprinting methodology where matching to each block a fingerprint is created. The keywords in each block are identified and the frequency of each keyword in a block is found out. The collection of fingerprints of blocks are analyzed. A block is considered as duplicate block if its fingerprint is different from other fingerprints by at most bit position where represents a small integer. Block importance is calculated based on keyword redundancy, link word percentage and title word relevancy. Keyword redundancy refers to the percentage of redundant words in a block. The percentage of link words in a block are also computed. Percentage of keywords present in a block is referred to as title word relevancy. A block is considered to be an important only if its block importance is greater than the threshold value. [4]
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A Keyword Based Educational and Non Educational Website Recognition Tool

A Keyword Based Educational and Non Educational Website Recognition Tool

In today‟s internet era, it is very difficult to stop students from accessing unwanted data. Students always access academic or non-academic information from internet. E-learning is one of the novel approach introduced using internet. This is one of the modern education systems which has many pros and cons. Advantage is that anyone can enroll in these online courses from absolutely anywhere. But the major disadvantage is that, students may get distracted from their studies very easily while surfing on the internet. The search engine “Google” always displays a list of related and unrelated websites in its search result. Students spend a lot of time on internet to search relevant data from educational sites. It is very difficult for them to classify the listed web sites are as learning or non learning.
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Improved Weight based Web Page Ranking Algorithm

Improved Weight based Web Page Ranking Algorithm

The main aim of data preprocessing is to optimize the content or data to become enable the algorithm to work effectively. Therefore, the text data is preprocessed in this phase for reducing the unwanted content and filter out the valuable content which can be utilized with algorithm for application point of view. Therefore, in this phase two key techniques are implemented. In first the stop words from the text data is removed. Additionally, in second method the special characters are removed. After filtering the web content, the remaining data is utilized in further phases for developing web page rank
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