Journal articles: 'Web usage log mining'

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Harika, B., and T. Sudha. "Extraction of Knowledge from Web Server Logs Using Web Usage Mining." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 12–15. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2113.

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Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

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Malik, Varun, Vikas Rattan, Jaiteg Singh, Ruchi Mittal, and Urvashi Tandon. "Performance Comparison of Data Mining Classifiers on Web Log Data." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 5113–16. http://dx.doi.org/10.1166/jctn.2020.9349.

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Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

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IJMTST061248. "Automated User Behavior Mapping Using Web Usage Mining." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 13, 2020): 257–61. http://dx.doi.org/10.46501/ijmtst061247.

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Automated User Behavior Mapping is an application of web usage mining using which we can see the real-time behavior of end user visiting a particular web page automatically. The technologies used in this are socket programming for real-time communication between the server and the user accessing the website for collection of web log data and selenium web driver for automating the user behavior using web log files.

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Kartina Diah Kusuma Wardani. "Ekstraksi Click Stream Data Web E-Commerce Menggunakan Web Usage Mining." Jurnal Informatika Polinema 7, no. 2 (February 23, 2021): 65–72. http://dx.doi.org/10.33795/jip.v7i2.538.

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E-Commerce berkembang pesat dalam world wide web hingga menghasilkan berbagai jenis data yang dapat dianalisa lebih lanjut untuk berbagai keperluan seperti personifikasi web, profiling customer, dan sebagainya. Salah satu jenis data yang dihasilkan e-Commerce adalah click stream data web yang merekam aktivitas visitor web dalam bentuk log data selama berinteraksi pada laman web. Penelitian ini mengekstraksi click stream data web e-commerce untuk mendapatkan pola interaksi konsumen terhadap halaman web selama mengunjungi web e-commerce. Berdasarkan jenis data yang diekstrak maka web usage mining digunakan untuk ekstraksi pola dari click stream data yang berbentuk log data. Teknik mining yang dianalisa terhadap log data e-commerce pada penelitian ini terdiri dari frequent itemset, asociation rules, dan frequence sequence mining. Frequent itemset menghasilkan halaman web yang paling sering diakses oleh visitor. Association rules menghasilkan pola kemungkinan halaman web yang akan diakses visitor jika visitor mengakses halaman-halamn tertentu. Frequence sequence mining mendapatkan pola urutan halaman web yang paling sering diakses oleh visitor web e-commerce saat berinteraksi pada laman web. Pola urutan halaman yang diakses visitor menunjukkan urutan kebiasaan visitor mengunjungi e-commerce. Sedangkan teknik mining yang diimplementasikan untuk menghasilkan pola akses visitor pada penelitian ini adalah Frequence sequence mining. Hasil ekstraksi dari penelitian ini menunjukkan ada enam halaman web yang paling sering diakses oleh konsumen dengan berbagai pola urutan aksesnya.

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Chai, Chun Lai. "A Heuristic Mining Algorithm Using Web Hyperlink Structure." Advanced Materials Research 108-111 (May 2010): 11–16. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.11.

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Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.

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R., Virendra, and Govind V. "Prediction of User Behavior using Web log in Web Usage Mining." International Journal of Computer Applications 139, no. 8 (April 15, 2016): 4–7. http://dx.doi.org/10.5120/ijca2016909228.

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Ramanathaiah, Ramakrishnan M., Bhawna Nigam, and M. Niranjanamurthy. "Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4432–37. http://dx.doi.org/10.1166/jctn.2020.9091.

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Web Usage Mining applies fewer techniques in record data to pull out the behavior of users. The knowledge mined from the web log can be utilized in web personalization, Prediction, prefetching, restructuring of web sites etc. It consists of three steps in preprocessing, pattern detection and analysis. Web log information is typically noisy and uncertain and preprocessing is a significant process ahead of mining. The Patterns discovered after applying the mining techniques are dependent on the accuracy of the weblog which in turn depends on the preprocessing phase. The output of preprocessing should be the user’s navigation session file. In this paper the techniques of preprocessing and the method for construction of user’s navigation session file is proposed.

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Ezzikouri, Hanane, Mohamed Fakir, Cherki Daoui, and Mohamed Erritali. "Extracting Knowledge from Web Data." Journal of Information Technology Research 7, no. 4 (October 2014): 27–41. http://dx.doi.org/10.4018/jitr.2014100103.

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The user behavior on a website triggers a sequence of queries that have a result which is the display of certain pages. The Information about these queries (including the names of the resources requested and responses from the Web server) are stored in a text file called a log file. Analysis of server log file can provide significant and useful information. 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 is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users' access models automatically and quickly from the vast Web log file, such as frequent access paths, frequent access page groups and user clustering. Through Web Usage Mining, several information left by user access can be mined which will provide foundation for decision making of organizations, Also the process of Web mining was defined as the set of techniques designed to explore, process and analyze large masses of consecutive information activities on the Internet, has three main steps: data preprocessing, extraction of reasons of the use and the interpretation of results. This paper will start with the presentation of different formats of web log files, then it will present the different preprocessing method that have been used, and finally it presents a system for “Web content and Usage Mining'' for web data extraction and web site analysis using Data Mining Algorithms Apriori, FPGrowth, K-Means, KNN, and ID3.

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PADMAKUMAR, SUJATHA, Dr PUNITHAVALLI Dr.PUNITHAVALLI, and Dr RANJITH Dr.RANJITH. "A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 92–94. http://dx.doi.org/10.15373/22778179/apr2014/34.

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Yau, Ng Qi, and Wan Zainon. "UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES." International Journal of Engineering Technologies and Management Research 4, no. 9 (February 1, 2020): 18–26. http://dx.doi.org/10.29121/ijetmr.v4.i9.2017.96.

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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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Raman, Gokulapriya, and Ganesh Raj. "Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 244–56. http://dx.doi.org/10.22266/ijies2021.0228.24.

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Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.

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Trakunphutthirak, Ruangsak, Yen Cheung, and Vincent C. S. Lee. "Conceptualizing Mining of Firm’s Web Log Files." Journal of Systems Science and Information 5, no. 6 (December 20, 2017): 489–510. http://dx.doi.org/10.21078/jssi-2017-489-22.

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AbstractIn this era of a data-driven society, useful data (Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information, however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

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Wang, Ting Zhong, and Gang Long Fan. "The Development and Design of Intelligent Web Site Based on Web Usage Mining." Advanced Materials Research 718-720 (July 2013): 2074–79. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2074.

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Web usage mining is the information about the user data extraction, transformation, analysis and model processing, extracted from the auxiliary business decision of key data. Intelligent site refers to the use of the Web server log for user access patterns and provide personalized service for users. The paper proposes the development and design of intelligent web site based on web usage mining. This paper presents the access interest measure method and the traditional consider only clicks visit interest measure method, recommend less deviation, has better recommendation results.

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BORGES, JOSÉ, and MARK LEVENE. "AN AVERAGE LINEAR TIME ALGORITHM FOR WEB USAGE MINING." International Journal of Information Technology & Decision Making 03, no. 02 (June 2004): 307–19. http://dx.doi.org/10.1142/s0219622004001021.

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In this paper, we study the complexity of a data mining algorithm for extracting patterns from user web navigation data that was proposed in previous work.3 The user web navigation sessions are inferred from log data and modeled as a Markov chain. The chain's higher probability trails correspond to the preferred trails on the web site. The algorithm implements a depth-first search that scans the Markov chain for the high probability trails. We show that the average behaviour of the algorithm is linear time in the number of web pages accessed.

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Li, Yun, Yongyao Jiang, Juan Gu, Mingyue Lu, Manzhu Yu, Edward Armstrong, Thomas Huang, et al. "A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch." Applied Sciences 9, no. 6 (March 16, 2019): 1114. http://dx.doi.org/10.3390/app9061114.

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The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion, and recommendation. While researches were done in the data discovery domain, collecting and analyzing logs efficiently remains a challenge because (1) the volume of web usage logs continues to grow as long as users access the data; (2) the dynamic volume of logs requires on-demand computing resources for mining tasks; (3) the mining process is compute-intensive and time-intensive. To speed up the mining process, we propose a cloud-based log-mining framework using Apache Spark and Elasticsearch. In addition, a data partition paradigm, logPartitioner, is designed to solve the data imbalance problem in data parallelism. As a proof of concept, oceanographic data search and access logs are chosen to validate performance of the proposed parallel log-mining framework.

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HUANG, XIANGJI. "COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 06, no. 01 (March 2007): 15–41. http://dx.doi.org/10.1142/s0219622007002368.

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A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection of Livelink log data. Livelink is a web-based product of Open Text Corporation, which provides automatic management and retrieval of different types of information objects over an intranet, an extranet or the Internet. We report our experience in preprocessing raw log data, mining association rules and sequential patterns from the log data, and identifying interesting rules and patterns by use of interestingness measures and some pruning methods. In particular, we evaluate a number of interestingness measures in terms of their effectiveness in finding interesting association rules and sequential patterns. Our results show that some measures are much more effective than others.

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., R. Sandrilla, and M. Savitha Devi. "A Study on Data Preprocessing Methods on Web Log Data in Web Usage Mining." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 920–28. http://dx.doi.org/10.26438/ijcse/v6i7.920928.

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Rao, Kannasani Srinivasa, M. Krishnamurthy, and A. Kannan. "Extracting the User’s Interests by Using Web Log Data Based on Web Usage Mining." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5031–40. http://dx.doi.org/10.1166/jctn.2015.4468.

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Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge present in the Web log files , in recognizing various interests of web users and also in discovering customer behaviours. Web Usage mining includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining algorithms like apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy, sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

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Srivastava, Mitali, Rakhi Garg, and P. K. Mishra. "A MapReduce-Based User Identification Algorithm in Web Usage Mining." International Journal of Information Technology and Web Engineering 13, no. 2 (April 2018): 11–23. http://dx.doi.org/10.4018/ijitwe.2018040102.

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This article contends that in the booming era of information, analysing users' navigation behaviour is an important task. User identification is considered as one of the important and challenging tasks in the data preprocessing phase of the Web usage mining process. There are three important issues with the reactive strategies of User identification methods that need to be focused: the first is dealing of sharing IP address problem in a proxy server environment, the second is distinguishing users from Web robots, and the third is dealing with huge datasets efficiently. In this article, authors have developed a MapReduce-based User identification algorithm that deals with the above mentioned three issues related to user identification methods. Moreover, the experiment on the real web server log shows the effectiveness and efficiency of the developed algorithm.

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HU, JIA, and NING ZHONG. "WEB FARMING WITH CLICKSTREAM." International Journal of Information Technology & Decision Making 07, no. 02 (June 2008): 291–308. http://dx.doi.org/10.1142/s0219622008002971.

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In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

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Ahuja, Dheeraj. "Tracking User Interaction with Web and Assisting in Targeted Communication." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 605–11. http://dx.doi.org/10.22214/ijraset.2021.35037.

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Today, we spend most of our time online using some form of digital technology (such as search engines, news portals, or social media sites). Our online presence keeps us involved most of the time and provides a lot of information to Internet customers. The development of the web is excellent because every day about a million pages are added. Due to the massive use of the network, the log files of the network increase at a faster rate and the scope becomes enormous. Web Usage Mining uses mining technology on log data to extract user performance, which is used in different applications such as support design, e-commerce, service modification, prefetch, etc. In this paper, we propose a tool that users can use to collect data on their website, and then use this web log data to track user interactions on your website, which helps in targeted communication.

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Kapusta, Jozef, Michal Munk, and Martin Drlik. "Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods." International Journal of Information Technology & Decision Making 17, no. 06 (November 2018): 1743–76. http://dx.doi.org/10.1142/s0219622018500402.

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The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

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Li, Bin, Jin Yang, Cai Ming Liu, Jian Dong Zhang, and Yan Zhang. "Research on Improved Clustering Algorithm on Web Usage Mining Based on Scientific Analysis of Web Materials." Applied Mechanics and Materials 63-64 (June 2011): 863–67. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.863.

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Clustering analysis is an important method to research the Web user’s browsing behavior and identify the potential customers on Web usage mining. The traditional user clustering algorithms are not quite accurate. In this paper, we give two improved user clustering algorithms, which are based on the associated matrix of the user’s hits in the process of browsing website. To this matrix, an improved Hamming distance matrix is generated by defining the minimum norm or the generalized relative Hamming distance between any two vectors. Then, similar user clustering are obtained by setting the threshold value. At the last step of our algorithm, the clustering results are confirmed by defining the clustering’s Similar Index and setting sub-algorithm. Finally, the testing examples show that the new algorithms are more accurate than the old one, and the real log data presents that the improved algorithms are practical.

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Librado, Dison, and Wagito Wagito. "PEMETAAN AKSES HALAMAN SITUS WEB BERBASIS LOG-ACCESS." Jurnal SAINTEKOM 9, no. 2 (October 12, 2019): 95. http://dx.doi.org/10.33020/saintekom.v9i2.78.

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Since the 2016, STMIK AKAKOM Library implements the information system thoroughly by using developed application that should connect to other library application. Various menu that provided is Home, Kontak, Tautan, Layanan, Profile, Katalog Online, and Digital Library. Research aims to determine the pattern of visits to the web and identify what pages are frequently visited by visitors Research begins with literature study of a relevant topic, configure the Nginx server, collecting log access data during a certain time, until prepared them so the result and conclusions can be achieve. In web usage mining implementation there are three stages that are carried out to get libraries and sources of information namely preprocessing, pattern discovery and pattern analysis. The research object is the STMIK AKAKOM library site. The results show which pages that most frequently visited is ‘Home’, ‘Berita’, ‘Digital Library’, ‘Koleksi’, Tautan’, ‘Layanan’, ‘Katalog Online’, dan ‘Kontak’. The operating system or browser that used much more is Windows, Android, Linux, and iphone. By Internet Protocol, the most visitors came in is from outside of Akakom

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Kapusta, Jozef, Anna Pilková, Michal Munk, and Peter Švec. "Data pre-processing for web log mining: Case study of commercial bank website usage analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 4 (2013): 973–79. http://dx.doi.org/10.11118/actaun201361040973.

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We use data cleaning, integration, reduction and data conversion methods in the pre-processing level of data analysis. Data processing techniques improve the overall quality of the patterns mined. The paper describes using of standard pre-processing methods for preparing data of the commercial bank website in the form of the log file obtained from the web server. Data cleaning, as the simplest step of data pre-processing, is non–trivial as the analysed content is highly specific. We had to deal with the problem of frequent changes of the content and even frequent changes of the structure. Regular changes in the structure make use of the sitemap impossible. We presented approaches how to deal with this problem. We were able to create the sitemap dynamically just based on the content of the log file. In this case study, we also examined just the one part of the website over the standard analysis of an entire website, as we did not have access to all log files for the security reason. As the result, the traditional practices had to be adapted for this special case. Analysing just the small fraction of the website resulted in the short session time of regular visitors. We were not able to use recommended methods to determine the optimal value of session time. Therefore, we proposed new methods based on outliers identification for raising the accuracy of the session length in this paper.

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PABARSKAITE, ZIDRINA, and JAMES ALLEN LONG. "DECISION TREES FOR PERNICIOUS PAGES DETECTION." International Journal on Artificial Intelligence Tools 12, no. 04 (December 2003): 527–37. http://dx.doi.org/10.1142/s0218213003001356.

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An application framework to perform web usage analysis using advanced data mining methodology is presented. The investigation proposes decision trees for web user behavior analysis. This includes prediction of user future actions and the typical pages leading to browsing termination. The widely known decision tree package C4.5 was used in this study. In the new area of web log mining decision trees showed reasonable computational performance and accuracy. Experiments showed that it is possible to predict future user actions with reasonable misclassification error as well as to find combinations of sequential pages resulting in browsing termination. In addition to this, decision trees generate human understandable rules which can be used to analyze further for web site improvement.

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Giannikopoulos, Panagiotis, Iraklis Varlamis, and Magdalini Eirinaki. "Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies." International Journal of Data Warehousing and Mining 6, no. 1 (January 2010): 58–76. http://dx.doi.org/10.4018/jdwm.2010090804.

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The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site.

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Pérez Niño, Álvaro. "Web usage mining para la identificación de patrones de comportamiento de usuarios mediante el uso de herramientas tecnológicas." Vía Innova 1, no. 1 (December 15, 2014): 14. http://dx.doi.org/10.23850/2422068x.363.

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Las herramientas de minería y análisis de datos, toman mayor relevancia cuando se afrontan grandes volúmenes de datos (Smyth, 2000) . En la actualidad los sitios y aplicaciones web ofrecen una gran oportunidad de contacto con los usuarios. Los servidores web registran de manera permanente los datos relacionados con las acciones realizadas por sus usuarios. Teniendo de esta manera registros que incluyen valores relacionados con la navegación de los clientes, tiempos de acceso, fechas, recursos buscados, respuestas del servidor ante las peticiones y orígenes de navegación. Es allí donde debemos aplicar los beneficios de la minería de datos y minería web y buscar el fortalecimiento a sistemas de información que ayuden a fortalecer las decisiones de la alta gerencia en las organizaciones (Y. M. Chae, 2003). El sitio web representa un canal de alto impacto en la medición de niveles de comunicación con la comunidad y en el Plan de Medios de la Organización se constituye como componente clave de la estrategia de difusión y de igual forma, contempla valores que pueden ayudar a comprender indicadores. Se resalta la importancia de los valores éticos y profesionales en el desarrollo del estudio (Drury, 2003). Abordamos el presente estudio procesando los datos almacenados en el log del servidor Web de un Hospital Público de Alta Complejidad del Municipio de Manizales Caldas y mediante el software libre Analog versión 6.0 y la versión de evaluación de Advanced Log Analyzer, visualizaremos información estadística que permitirá a la Institución tomar acciones frente a los patrones y comportamiento de los usuarios que navegan el website; mediante la generación de reglas de decisión y técnicas de agrupamiento.

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Kurniawati, Galuh Nurvinda, Rukun Santoso, and Sugito Sugito. "ANALISIS WEB USAGE MINING MENGGUNAKAN METODE MODIFIED GUSTAFSON – KESSEL CLUSTERING DAN ASSOCIATION RULE PADA WEBSITE UNIVERSITAS DIPONEGORO." Jurnal Gaussian 9, no. 4 (December 7, 2020): 486–94. http://dx.doi.org/10.14710/j.gauss.v9i4.29446.

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The comprehension of web visitors patterns are needed to develop website in an optimal fashion. The visitor pattern contained in the web log file of Diponegoro University’s website is clustered by Modified Gustafson-Kessel method. In general, this method produces two until six clusters. Two kinds of results are outlined in this paper. The first is the result contains two clusters, and the second is containing three clusters. In the first result, the visitors are divided into information seekers of student capacity and Engineering Faculty. In the second result, the visitors are divided into information seekers of Medicine Faculty, student admission and Engineering Faculty.

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Gandhimathi, D., and N. Anbazhagan. "Extracting of Positive and Negative Association Rules." International Journal of Emerging Research in Management and Technology 6, no. 8 (June 25, 2018): 421. http://dx.doi.org/10.23956/ijermt.v6i8.175.

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Association rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets. In this paper, we propose association rules from new methods on web usage mining. Generally, web usage log structure has several records so we have to overcome those unwanted records from large dataset. First of all the pre-processed data from the NASA dataset is clustered by the popular K-Means algorithm. Subsequently, the matrix calculation is progressed on that data. Further, the associations are performed on filtered data and get rid of the final associated page results. Positive and negative association rules are gathered by using new algorithm with Annul Object (𝒜𝒪). Wherever the object “𝒜𝒪” is presented those rules are known as negative association rule. Otherwise, the rules are positive association rules.

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OH, SEUNG-JOON, and JAE-YEARN KIM. "A SCALABLE CLUSTERING METHOD FOR CATEGORICAL SEQUENCE DATA." International Journal of Computational Methods 02, no. 02 (June 2005): 167–80. http://dx.doi.org/10.1142/s0219876205000417.

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Clustering of sequences is relatively less explored but it is becoming increasingly important in data mining applications such as web usage mining and bioinformatics. The web user segmentation problem uses web access log files to partition a set of users into clusters such that users within one cluster are more similar to one another than to the users in other clusters. Similarly, grouping protein sequences that share a similar structure can help to identify sequences with similar functions. However, few clustering algorithms consider sequentiality. In this paper, we study how to cluster sequence datasets. Due to the high computational complexity of hierarchical clustering algorithms for clustering large datasets, a new clustering method is required. Therefore, we propose a new scalable clustering method using sampling and a k-nearest-neighbor method. Using a splice dataset and a synthetic dataset, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional algorithms.

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Vasconcelos, Leandro Guarino, Laercio Augusto Baldochi, and Rafael Duarte Coelho Santos. "An approach to support the construction of adaptive Web applications." International Journal of Web Information Systems 16, no. 2 (February 26, 2020): 171–99. http://dx.doi.org/10.1108/ijwis-12-2018-0089.

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Purpose This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web applications. The main goal of RUM is to provide useful information about the behavior of users that are currently browsing a Web application. By consuming this information, the application is able to adapt its user interface in real-time to enhance the user experience. RUM provides two types of services as follows: support for the detection of struggling users; and user profiling based on the detection of behavior patterns. Design/methodology/approach RUM leverages the previous study on usability evaluation to provide a service that evaluates the usability of tasks performed by users while they browse applications. This evaluation is based on a metric that allows the detection of struggling users, making it possible to identify these users as soon as few logs from their interaction are processed. RUM also exploits log mining techniques to detect usage patterns, which are then associated with user profiles previously defined by the application specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse applications, allowing the application developer to tailor the user interface according to the users’ needs and preferences. Findings The proposed approach was exploited to improve user experience in real-world Web applications. Experiments showed that RUM was effective to provide support for struggling users to complete tasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles. Originality/value Although the literature reports studies that explore client logs to support both the detection of struggling users and the user profiling based on usage patterns, no existing solutions provide support for detecting users from specific profiles or struggling users, in real-time, while they are browsing Web applications. RUM also provides a toolkit that allows the approach to be easily deployed in any Web application.

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Sukoco, Sutrisno Heru, Imas Sukaesih Sitanggang, and Heru Sukoco. "ANALISIS KINERJA PEGAWAI PUSBINDIKLAT PENELITI LIPI BERDASARKAN POLA PEMANFAATAN INTERNET MELALUI PENDEKATAN WEB USAGE MINING." Jurnal Penelitian Pos dan Informatika 8, no. 2 (December 25, 2018): 141. http://dx.doi.org/10.17933/jppi.2018.080204.

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Pengukuran kinerja pegawai dalam penggunaan layanan internet dapat dilakukan sebagai bagian dari penilaian kinerja. Pendekatan web usage mining melalui pengamatan rekam jejak akses internet yang tersimpan pada proxy server merupakan salah satu cara yang dapat diterapkan untuk memahami perilaku pengguna. Penelitian ini bertujuan untuk mendapatkan gambaran perilaku pegawai Pusbindiklat Peneliti LIPI dalam memanfaatkan layanan internet, mengukur level produktivitas pegawai berdasarkan lama waktu akses terhadap situs yang tidak mendukung pekerjaan dan memetakan kategori situs yang diakses apakah medukung tugas fungsi jabatannya. Penerapan algoritme clustering K-Means digunakan untuk memudahkan memahami pola akses pengguna. Data yang digunakan adalah log proxy server dan nilai prilaku pegawai Pusbindiklat Peneliti LIPI periode Agustus-Desember 2016. Hasil penelitian menunjukkan pola pemanfaatan internet oleh pegawai Pusbindiklat Peneliti LIPI belum sepenuhnya mendukung tugas fungsi jabatannya. Sekitar 83% pegawai menggunakan internet untuk mengakses situs yang tidak mendukung pekerjaan berada pada level rendah (0-4 jam per minggu). Berdasarkan hasil tersebut dapat disimpulkan bahwa prilaku penggunaan internet yang dilakukan pegawai Pusbindiklat Peneliti LIPI tidak mempengaruhi produktivitas secara signifikan.<div> </div><div>AbstractMeasurement of employee performance in the use of internet services can be conducted as part of employee’s performance target. Web usage mining approach through observation of internet access records stored in the proxy server can be applied in understanding user behavior. This study aims to obtain an overview of employee behavior in utilizing internet services in Pusbindiklat Peneliti LIPI, measure the level of employee productivity based on the length of time access to sites that do not support the work and map the category of sites accessed to the task dutyof employee. K-Means clustering algorithm is used to group user access patterns. The data used are proxy server logs and employee’s performance target in Pusbindiklat Peneliti LIPI in period of August-December 2016. The results shows that the pattern of Internet use by employees Pusbindiklat Peneliti LIPI do not fully support the job function. About 83% of employees use the internet to access sites do not support jobs at low level access (ranging from 0-4 hours per week). Based on these results, it can be concluded that the behavior of internet use by employees of Pusbindiklat Peneliti LIPI does not affect their productivity significantly. Keywords: clustering, K-Means, log proxy server, performance of employees, web usage mining </div>

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Popelka, Ondřej, and Jiří Šťastný. "WWW portal usage analysis using genetic algorithms." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 6 (2009): 201–8. http://dx.doi.org/10.11118/actaun200957060201.

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The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors’ behaviour in different manners depending on desired further processing. In this article we use description with a procedural programming language. Web server access log files are used as source data.The extraction of behaviour patterns can currently be solved using statistical analysis – specifically sequential analysis based methods. Our objective is to develop an alternative algorithm.The article further describes the basic algorithms of two-level grammatical evolution; this involves basic Grammatical Evolution and Differential Evolution, which forms the second phase of the computation. Grammatical evolution is used to generate the basic structure of the solution – in form of a part of application code. Differential evolution is used to find optimal parameters for this solution – the specific pages visited by a random visitor. The grammar used to conduct experiments is described along with explanations of the links to the actual implementation of the algorithm. Furthermore the fitness function is described and reasons which yield to its’ current shape. Finally the process of analyzing and filtering the raw input data is described as it is vital part in obtaining reasonable results.

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Priyanto, Edi, Arief Hermawan, Rianto Rianto, and Donny Avianto. "Efektifitas Penggunaan Association Rules Mining dalam Personalisasi Website." JISKA (Jurnal Informatika Sunan Kalijaga) 6, no. 1 (January 20, 2021): 59–69. http://dx.doi.org/10.14421/jiska.2021.61-07.

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As the usage of the internet grows, more and more information is obtained, thus presenting challenges, especially for users and website owners. Website users often have difficulty finding products or services that are relevant to their needs caused by abundant amounts of products and services delivered on a website. Website owners often find it difficult to convey information about the right products and services to certain target users. Based on the problem given above, we can conclude that a recommendation system approach that can improve personalization on their website is needed. The recommendation system approach must be able to provide navigation on the website to make it more adaptive towards the interests and information needed by the user. This study uses Association Rules formed from Microsoft web access log data by finding visitor patterns based on frequently visited web site pages. From the results of the research conducted, the performance of the method used has a precision value of 0.896, 0.058 recall, and F-measure 0.104. Whereas the measurement of the accuracy value resulted in a performance recommendation of exactly 3%, an acceptable rate of 87%, and 10% incorrect. This research shows that the Association Rules method can increase the effectiveness of website personalization to provide relevant information recommendations for visitors. For further research, it can concentrate on improving existing methods thus website personalization becomes more adaptive.

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Mohamad Mohsin, Mohamad Farhan, Mohd Noor Abdul Hamid, Nurakmal Ahmad Mustaffa, Razamin Ramli, and Kamarudin Abdullah. "Investigation on the Access Log Pattern of the Corporate Social Responsibility UUMWiFi among Changlun’s Community." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 04 (April 10, 2019): 140. http://dx.doi.org/10.3991/ijim.v13i04.10526.

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CSR UUMWiFi is a CSR project under Universiti Utara Malaysia (UUM) that provides unlimited free internet connection for the Changlun community. Launched in 2015, the service has accumulated a huge number of users with diverse background and interest. This paper aims to uncover interesting service users’ behavior by mining the usage data. To achieve that, the access log for 3 months with 24,000 online users were downloaded from the Wi-Fi network server, pre-process and analyzed. The finding reveals that there were many loyal users who have been using this service on a daily basis since 2015 and the community spent 20-60 minutes per session. Besides that, the social media and leisure based application such YouTube, Facebook, Instagram, chatting applications, and miscellaneous web applications were among the top applications accessed by the Changlun community which contributes to huge data usage. It is also found that there were few users have used the CSR UUM WiFi for academic or business purposes. The identified patterns benefits the management team in providing a better quality service for community in future and setting up new policies for the service.

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Trakunphutthirak, Ruangsak, Yen Cheung, and Vincent C. S. Lee. "A Study of Educational Data Mining: Evidence from a Thai University." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 734–41. http://dx.doi.org/10.1609/aaai.v33i01.3301734.

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Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

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Di Tosto, Gennaro, Ann Scheck McAlearney, Naleef Fareed, and Timothy R. Huerta. "Metrics for Outpatient Portal Use Based on Log File Analysis: Algorithm Development." Journal of Medical Internet Research 22, no. 6 (June 12, 2020): e16849. http://dx.doi.org/10.2196/16849.

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Background Web-based outpatient portals help patients engage in the management of their health by allowing them to access their medical information, schedule appointments, track their medications, and communicate with their physicians and care team members. Initial studies have shown that portal adoption positively affects health outcomes; however, early studies typically relied on survey data. Using data from health portal applications, we conducted systematic assessments of patients’ use of an outpatient portal to examine how patients engage with the tool. Objective This study aimed to document the functionality of an outpatient portal in the context of outpatient care by mining portal usage data and to provide insights into how patients use this tool. Methods Using audit log files from the outpatient portal associated with the electronic health record system implemented at a large multihospital academic medical center, we investigated the behavioral traces of a study population of 2607 patients who used the portal between July 2015 and February 2019. Patient portal use was defined as having an active account and having accessed any portal function more than once during the study time frame. Results Through our analysis of audit log file data of the number and type of user interactions, we developed a taxonomy of functions and actions and computed analytic metrics, including frequency and comprehensiveness of use. We additionally documented the computational steps required to diagnose artifactual data and arrive at valid usage metrics. Of the 2607 patients in our sample, 2511 were active users of the patients portal where the median number of sessions was 94 (IQR 207). Function use was comprehensive at the patient level, while each session was instead limited to the use of one specific function. Only 17.45% (78,787/451,762) of the sessions were linked to activities involving more than one portal function. Conclusions In discussing the full methodological choices made in our analysis, we hope to promote the replicability of our study at other institutions and contribute to the establishment of best practices that can facilitate the adoption of behavioral metrics that enable the measurement of patient engagement based on the outpatient portal use.

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Lin, Jerry Chun-Wei, Wensheng Gan, Tzung-Pei Hong, and Jingliang Zhang. "Updating the Built Prelarge Fast Updated Sequential Pattern Trees with Sequence Modification." International Journal of Data Warehousing and Mining 11, no. 1 (January 2015): 1–22. http://dx.doi.org/10.4018/ijdwm.2015010101.

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Mining useful information or knowledge from a very large database to aid managers or decision makers to make appropriate decisions is a critical issue in recent years. Sequential patterns can be used to discover the purchased behaviors of customers or the usage behaviors of users from Web log data. Most approaches process a static database to discover sequential patterns in a batch way. In real-world applications, transactions or sequences in databases are frequently changed. In the past, a fast updated sequential pattern (FUSP)-tree was proposed to handle dynamic databases whether for sequence insertion, deletion or modification based on FUP concepts. Original database is required to be re-scanned if it is necessary to maintain the small sequences which was not kept in the FUSP tree. In this paper, the prelarge concept was adopted to maintain and update the built prelarge FUSP tree for sequence modification. A prelarge FUSP tree is modified from FUSP tree for preserving not only the frequent 1-sequences but also the prelarge 1-sequences in the tree structure. The PRELARGE-FUSP-TREE-MOD maintenance algorithm is proposed to reduce the rescans of the original database due to the pruning properties of prelarge concept. When the number of modified sequences is smaller than the safety bound of the prelarge concept, better results can be obtained by the proposed PRELARGE-FUSP-TREE-MOD maintenance algorithm for sequence modification in dynamic databases.

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Ahmed, Moiz Uddin, and Amjad Mahmood. "Web Usage Mining." International Journal of Technology Diffusion 3, no. 3 (July 2012): 1–12. http://dx.doi.org/10.4018/jtd.2012070101.

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The technological revolutions have opened up new ways of information and communication. The Internet is growing as a vital source of information in this modern era of technology. The ever increasing volume of information through WWW is creating complexity in the design, development and deployment of WWW. It has become important for the organizations to analyze the usage of their web sites. The web usage analysis may help the organizations not only to monitor the load on their websites and cater for the needs of their potential clients but also enhance their web services and restructure the organization to better serve their clients. Web mining has emerged as important research areas used to discover information which can be utilized for improvement of websites. Allama Iqbal Open University (AIOU) is one of the largest open and distant university of the world. Due to unique philosophy of open and distant learning, AIOU has been providing useful information online through its website. It is an active website which is flooded with huge flow of information. This paper presents web usage analysis of AIOU website and provides statistical analysis of the usage patterns. It presents how the results were used not only to enhance the web contents and services but also discusses how these results helped the university to allocate and reallocate its resources. The reallocation was used to improve efficiency and processes of the university in order to better serve its clients.

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Hippner, Hajo, Melanie Merzenich, and Klaus D. Wilde. "Web Usage Mining." WiSt - Wirtschaftswissenschaftliches Studium 31, no. 2 (2002): 105–10. http://dx.doi.org/10.15358/0340-1650-2002-2-105.

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Srivastava, Jaideep, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. "Web usage mining." ACM SIGKDD Explorations Newsletter 1, no. 2 (January 2000): 12–23. http://dx.doi.org/10.1145/846183.846188.

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Tseng, Woan-Rou, and Kuo-Wei Hsu. "Smartphone App Usage Log Mining." International Journal of Computer and Electrical Engineering 6, no. 2 (2014): 151–56. http://dx.doi.org/10.7763/ijcee.2014.v6.812.

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Qiang Yang and H. H. Zhang. "Web-log mining for predictive web caching." IEEE Transactions on Knowledge and Data Engineering 15, no. 4 (July 2003): 1050–53. http://dx.doi.org/10.1109/tkde.2003.1209022.

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Khatoon, Asfiya, and Kuldeep Jaiswal. "Web Page Ranking using Web Usage Mining." IJARCCE 6, no. 4 (April 30, 2014): 807–15. http://dx.doi.org/10.17148/ijarcce.2017.64150.

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Spiliopoulou, Myra. "Web usage mining for Web site evaluation." Communications of the ACM 43, no. 8 (August 2000): 127–34. http://dx.doi.org/10.1145/345124.345167.

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Pooja, Pooja. "Web Usage Mining: An Approach." International Journal of Computer Applications 86, no. 12 (January 16, 2014): 39–42. http://dx.doi.org/10.5120/15041-3387.

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Li, Dong, Anne Laurent, and Pascal Poncelet. "WebUser: mining unexpected web usage." International Journal of Business Intelligence and Data Mining 6, no. 1 (2011): 90. http://dx.doi.org/10.1504/ijbidm.2011.038276.

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Tripathi, Rajni, Munesh Chandra Trivedi, and Shraddha Tripathi. "Web Usage Mining: A Fact Finding Approach in Web Mining." International Journal of Computer Trends and Technology 12, no. 2 (June 25, 2014): 99–103. http://dx.doi.org/10.14445/22312803/ijctt-v12p119.

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Journal articles on the topic 'Web usage log mining'

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