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A Time Efficient Algorithm for Web Log Analysis

A Time Efficient Algorithm for Web Log Analysis

The world of web is very diverse and contains huge amount of information. To obtain relevant information from the www is a challenging task today. To ensure the retrieval of pertinent information, suitable web mining techniques can be applied. From the perspective of a web designer it is important to know about the web surfing behavior of a user which can be accomplished by using the web log that captures the history of user activities. There are a number of algorithms which can be used for analyzing web log, however choosing the appropriate algorithm is important to retrieve the required data in less amount of time. This paper proposes a new time efficient algorithm which is work for web log analysis and is very time efficient to the other web log algorithms.
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Model Survey on Web Usage Mining and Web Log Mining

Model Survey on Web Usage Mining and Web Log Mining

In the data preprocessing, it takes web log data as input and then process the web log data and gives the reliable data. The goal of preprocessing is to choose primary features, then remove unwanted information and finally transform raw data into sessions. So to do this Data preprocessing is divided into sub processes which are known as Data Cleaning, user identification, and Session Identification [12] [2].

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Web Log Mining using K Apriori Algorithm

Web Log Mining using K Apriori Algorithm

Web is a collection of inter-related files on one or more Web servers. Web mining discovers and extracts useful information from the World Wide Web (WWW) documents and services using the data mining techniques. Most users obtain WWW information using a combination of search engines and browsers; however these two types of retrieval mechanism do not address all of a user's information needs. The resulting growth in on-line information combined with the almost unstructured web data necessitates the development of computationally efficient web mining tools. Web Mining can be classified [1] as, web content mining, web structure mining and web usage mining. Web content mining means automatic search of information resources available online [2], in short, mining the data on the Web. Web structure mining means mining the web document's structure and links, in short, mining the Web structure data. Web usage mining includes the data from server access logs, user registration or profiles, user sessions or transactions, in short, mining the Web log data. Web mining subtasks are (a) resource finding and retrieving, (b) information selection and pre-processing, (c) patterns analysis and recognition, (d) validation and interpretation, and (e) visualization [3].
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Real Time Web Log Analysis and Hadoop for Data Analytics on Large Web Logs

Real Time Web Log Analysis and Hadoop for Data Analytics on Large Web Logs

sector are making a strategic decision to use the web log generated to gain competitive advantage. The main hurdle is to process this huge data efficiently for analytics purpose. It can be achieved by mining of this data. Web Log analyser is a tool used for finding the statics of web sites. Our project aims at implementing the web log analyzer for handling exception and errors. The web log data will be of unstructured form having XML data. Through Web Log analyzer the web log files are uploaded into the Hadoop Distributed Framework where parallel procession on log files is carried in the form of master and slave structure. Various association rule mining algorithms available will be applied onto this web log. The results of each implementation of algorithms would be noted. Finally, the noted results will be analyzed to find out the most suitable algorithm in terms of time and computational efficiency. The web log will be split up using Hadoop and then performed upon. Hadoop is the core platform for structuring big data and also used for analytics purpose. It will be a deep study of these algorithms along with the use of Hadoop during this process. This paper discuss about these log files, their formats, access procedures, their uses, the additional parameters that can be used in the log files which in turn gives way to an effective mining and the tools used to process the log files. It also provides the idea of creating an extended log file and learning the user behaviour. Also this paper presents the details of the working of by web log analyzer. In
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Enhanced Web Mining Technique To Clean Web Log File

Enhanced Web Mining Technique To Clean Web Log File

The information that can be accessed through web is heterogeneous and semi structured or unstructured in nature. Due to this heterogeneity a web log file may consists of some undesirable log entries whose presence does not matters from the web usage mining point of view. This makes the preprocessing of log file an important precondition for discovering the knowledgeable patterns. The purpose of performing preprocessing is to transform the raw click stream data into a set of user profiles.

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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

Cleaned data after preprocessing is solid base for pattern mining and pattern analysis. Quality of pattern mining and pattern analysis is fully dependent on preprocessing process. In this survey, we summarized the existing web log preprocessing techniques and concluded some results. Most authentic source for web usage mining considers Server log file. So it must be standardized and needs to be updated to capture user access data. Some of preprocessing techniques are applied but we can use less or even ignored preprocessing techniques to improve the quality of preprocessed data. For future work we should explore preprocessing techniques and use them with the combination of existing techniques to make the whole process more robust. New techniques can provide the user to analyze the log file at different level of abstraction such as user sessions. To gain better understanding of log file we need hierarchical clustering by using proposed clustering technique.
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Web Log Mining Operations for Classification of Product Reviews

Web Log Mining Operations for Classification of Product Reviews

The next step is to clean the log file. All the unwanted entries from the log file is removed and only the relevant entries are kept for future steps. This can be done by observing the extension (.gif, .html, .mp3, .mp4, etc.) of the web files requested by the user which is recorded in the web log file. For our model we kept only those entries having .html extension.

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Improved Web Prediction Algorithm Using Web Log Data

Improved Web Prediction Algorithm Using Web Log Data

In this paper, a user navigation pattern prediction system was presented which predict user future request. The Proposed system consists of mainly Five steps. In first step data is collected from Web Log file and that data is preprocessed to reduce size of file. In Second step users are classified into potential user and non potential user. In the third step clustering is done using graph partitioning algorithm and users having similar behaviors are grouped into one cluster. In fourth step backtracking algorithm is used. In fifth step User’s future request is predicted. Proposed System also uses backtracking algorithm which improves the performance and decreases the time complexity.
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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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Pre-Processing: Procedure on Web Log File for Web Usage Mining

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

Abstract— These days World Wide Web becomes very popular and interactive for transferring of Information. 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 phases, preprocessing, pattern discovery, and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use these information for the specific needs. The web access log file is saved to keep a record of every request made by the users. However, the data stored in the log files does not specify accurate details of the users’ accesses to the Web site. So, preprocessing of the Web log data is first and important phase before web log file can be applied for pattern analysis & pattern discovery. The preprocessed Web Log file can then be suitable for the discovery and analysis of useful information referred to as Web mining. This paper gives detailed description of how pre-processing is done on web log file and after that it is sent to next stages of web usage mining.
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Extraction of Frequent Patterns from Web Logs using Web Log Mining Techniques

Extraction of Frequent Patterns from Web Logs using Web Log Mining Techniques

World Wide Web is a huge repository of web pages and links. It provides profusion of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. User’s accesses are recorded in web logs. Because of the incredible usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web log Mining applies mining techniques in log data to extract the behaviour of users. Web log mining consists of three phases pre-processing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and pre-processing is an important process before web log mining. For discovering patterns sessions are to be constructed efficiently. This paper presents the existing work done to extracting patterns by using decision tree methodology in the technique of web log mining.
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An Enhanced Methods of Preprocessing in Web Log Mining

An Enhanced Methods of Preprocessing in Web Log Mining

interested in; not referred to the detected frequent visit page group which users are relatively interested in; refers to the detected frequent visit page group which users are interested in. Through Table 1-3, it is observed that, after the application of ID3 in Frame filtration, the quality of data preprocessing results is improved, meanwhile, it increased the interest degree of the mining results in large degree. Due to reducing of the step of upgrading site structure, this improved method increased the preprocessing efficiency, thereby, increased the mining efficiency of the whole web log.
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Customizable Web Log Mining from Web Server Log

Customizable Web Log Mining from Web Server Log

Abstract— Web Log file contain information about User Name, IP Address, Time Stamp, Access Request, number of Bytes transferred, Result Status, URL that Referred and User Agent as per user requirements. The log files are maintained by the web servers. By analyzing these log files gives a neat idea about the user behavior. This paper gives a detailed discussion about these log files, their formats, their creation, access procedures, their uses, various algorithms used and the additional parameters that can be used in the log files which in turn gives way to an effective mining. So the user can identify and analyze meaningful data. It also provides the idea of creating an extended log file and learning the user [1] .
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PERFORMANCE OF K-MEANS CLUSTERING AND BIRD FLOCKING ALGORITHM FOR GROUPING THE WEB LOG FILES

PERFORMANCE OF K-MEANS CLUSTERING AND BIRD FLOCKING ALGORITHM FOR GROUPING THE WEB LOG FILES

pages are accessing by whom, how much time user is accessing a particular page. Since log files are unformatted text files, they are complex to understand and study. So, the Web log files are preprocessed to eliminate the unwanted information to make the log files easy to handle. The main aim of this paper is to cluster the web log files based on the user’s interesting rate of accessing the web sites by using K-Means clustering algorithm and Bird flocking algorithm. The performance of both the algorithms is compared with the measures (i) no of clusters (ii) CPU time and (iii) Accuracy.
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A Survey on Web Log Mining Pattern Discovery

A Survey on Web Log Mining Pattern Discovery

The temporal nature of the events themselves, however, can likely be misinterpreted by current algorithms. We present a new definition of the temporal aspects of events and extend related work for pattern finding not only by making use of intervals between events but also by utilizing temporal relations like meets, starts, or during. The result is a new algorithm for Temporal Data Mining that preserves and mines additional time-oriented information. [21] Also in “Effect of Temporal Relationships in Associative Rule Mining for Web Log Data” by Nazli Mohd Khairudin, Aida Mustapha, and Mohd Hanif Ahmad it has been discussed that with The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analyzed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality [22].
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A SURVEY ON WEB LOG MINING AND PATTERN PREDICTION

A SURVEY ON WEB LOG MINING AND PATTERN PREDICTION

Web log mining technique is apply to improve web services and mining web navigation patterns efficiently. Sagar More [3] proposed a method for mining path traversal patterns. In that paper they first clear the concept of throughout surfing pattern (TSP) which predict the path of website visitor. In next part they apply modified graph traverse algorithm to make mining from TSP in efficient manner. The modified algorithm use the filtering technique which removes duplicate and unwanted data in web browsing session and show the effectiveness as compared to formal algorithms.
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Web Log Based Analysis of User’s Browsing Behavior

Web Log Based Analysis of User’s Browsing Behavior

With ample amounts of information present on the World Wide Web (WWW), issues relating to acquir- ing useful data from the Web has mounted the atten- tion among researchers in the field of knowledge discovering and mining of data. In today’s agonisti- cally business environment, Web services have be- come an implicit need for the organizations for dis- covering patterns. Knowledge acquired from the data which is collected helps in developing strategies for business. To create faithful customers and gain mili- tant advantage organizations are implementing value- added services. By providing personalized products and services, the companies are creating long-term relationships with users. By focusing on each indi- viduals need this type of personalization can be achieved. Web mining helps to retrieve such know- ledge for personalization and improved Web services. Web mining pertains to Knowledge Discovery in Data (KDD) from the web. That means, it is the process of application of data mining technique to retrieve useful information from immense amount of data available from web. Web mining and data min- ing objective being same both try searching for vari- able and useful information from web log and data- bases.
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Prediction of User Browsing Behavior Using Web Log Data

Prediction of User Browsing Behavior Using Web Log Data

Nowadays, Internet is a part and parcel of our life without having it we couldn’t imagine our life. When a user can access the web pages, they left some imperative information stored in their web log. This content is very useful and important in determining the web page navigational pattern of user. Web usage mining consist of three phases i.e. data pre-treatment, pattern discovery, pattern analysis. Firstly, all the web log data is being under data pre-treatment to retrieve logs with minimum redundancies and user session. Secondly, pattern discovery is used to extract user navigation patterns. At last, pattern analyzing algorithm is applied to extract data for data mining applications.
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ENHANCING THE ACCESSIBILITY AND USABILITY OF WEBSITE USING WEB LOG ANALYSIS

ENHANCING THE ACCESSIBILITY AND USABILITY OF WEBSITE USING WEB LOG ANALYSIS

There is much necessity to study web user conduct to better serve the users and increase the value of institution or enterprises. The web site design is currently based on entire investigation about the interest of web site visitors and investigated supposition about their shrewd conduct. The analysis of web log data allows to identify useful pattern of the browsing conduct of users, which exploited in the process of navigation of conduct. Web log data holds web browsing conduct of user from a web site. Educational institution is good example that develop web site. This paper present visitor pattern analysis performed through educational institution web log data.
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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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