Relevant bibliographies by topics / Web usage log mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Web usage log mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Web usage log mining"

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Khairo-Sindi, Mazin Omar. "Framework for web log pre-processing within web usage mining." Thesis, University of Manchester, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488456.

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Web mining is gaining popularity by the day and the role of the web in providing invaluable information about users' behaviour and navigational patterns is now highly appreciated by information technology specialists and businesses alike. Nevertheless, given the enormity of the web and the complexities involved in delivering and retrieving electronic information, one can imagine the difficulties involved in extracting a set of minable objects from the raw and huge web log data. Added to the fact that web mining is a new science, this may explain why research on data pre-processing is still limited in scope. And, although the debate on major issues is still gaining momentum, attempts to establish a coherent and accurate web usage pre-processing framework are still non existent. As a contribution to the existing debate, this research aims at formulating a workable, reliable, and coherent pre-processing framework. The present study will address the following issues: enhance and maximise knowledge about every visit made to a given website from multiple web logs even when they have different schemas, improve the process of eliminating excessive web log data that are not related to users' behaviour, modify the existing approaches for session identification in order to obtain more accurate results and eliminate redundant data that comes as a result of repeatedly adding cached data to the web logs regardless whether or not the added page is a frameset. In addition to the suggested improvements, the study will also introduce a novel task, namely, "automatic web log integration". This will make it possible to integrate different web logs with different schemas into a unified data set. Finally, the study will incorporate unnecessary information, particularly that pertaining to malicious website visits into the non user request removal task. Put together, both the suggested improvements and novel tasks result into a coherent pre-processing framework. To test the reliability and validity of the framework, a website is created in order to perform the necessary experimental work and a prototype pre-processing tool is devised and employed to support it.
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Shun, Yeuk Kiu. "Web mining from client side user activity log /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20SHUN.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 85-90). Also available in electronic version. Access restricted to campus users.
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Vlk, Vladimír. "Získávání znalostí z webových logů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236196.

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This master's thesis deals with creating of an application, goal of which is to perform data preprocessing of web logs and finding association rules in them. The first part deals with the concept of Web mining. The second part is devoted to Web usage mining and notions related to it. The third part deals with design of the application. The forth section is devoted to describing the implementation of the application. The last section deals with experimentation with the application and results interpretation.
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Tanasa, Doru. "Web usage mining : contributions to intersites logs preprocessing and sequential pattern extraction with low support." Nice, 2005. http://www.theses.fr/2005NICE4019.

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Le Web Usage Mining (WUM), domaine de recherche assez récent, correspond au processus d’extraction des connaissances à partir des données (ECD) appliquées aux données d’usage sur le Web. Il comporte trois étapes principales : le prétraitement des données, la découverte des schémas et l’analyse des résultats. La quantité des données d’usage à analyser ainsi que leur faible qualité (en particulier l’absence de structuration) sont les principaux problèmes en WUM. Les algorithmes classiques de fouille de données appliquées sur ces données donnent généralement des résultats décevants en termes de pratiques des internautes. Dans cette thèse, nous apportons deux contributions importantes pour un processus WUM, implémentées dans notre boîte à outils Axislogminer. D’abord, nous proposons une méthodologie générale de prétraitement des logs Web dont l’originalité consiste dans le fait qu’elle prend en compte l’aspect multi-sites du WUM. Nous proposons dans notre méthodologie quatre étapes distinctes : la fusion des fichiers logs, le nettoyage, la structuration et l’agrégation des données. Notre deuxième contribution vise à la découverte à partir d’un fichier log prétraité de grande taille, des comportements minoritaires correspondant à des motifs séquentiels de très faible support. Pour cela, nous proposons une méthodologie générale visant à diviser le fichier log prétraité en sous-logs, se déclinant selon trois approches d’extraction de motifs séquentiels au support faible (séquentielle, itérative et hiérarchique). Celles-ci ont été implémentées dans des méthodes concrètes hybrides mettant en jeu des algorithmes de classification et d’extraction de motifs séquentiels
The Web use mining (WUM) is a rather research field and it corresponds to the process of knowledge discovery from databases (KDD) applied to the Web usage data. It comprises three main stages : the pre-processing of raw data, the discovery of schemas and the analysis (or interpretation) of results. The quantity of the web usage data to be analysed and its low quality (in particular the absence of structure) are the principal problems in WUM. When applied to these data, the classic algorithms of data mining, generally, give disappointing results in terms of behaviours of the Web sites users (E. G. Obvious sequential patterns, stripped of interest). In this thesis, we bring two significant contributions for a WUM process, both implemented in our toolbox, the Axislogminer. First, we propose a complete methodology for pre-processing the Web logs whose originality consists in its intersites aspect. We propose in our methodology four distinct steps : the data fusion, data cleaning, data structuration and data summarization. Our second contribution aims at discovering from a large pre-processed log file the minority behaviours corresponding to the sequential patterns with low support. For that, we propose a general methodology aiming at dividing the pre-processed log file into a series of sub-logs. Based on this methodology, we designed three approaches for extracting sequential patterns with low support (the sequential, iterative and hierarchical approaches). These approaches we implemented in hybrid concrete methods using algorithms of clustering and sequential pattern mining
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Kilic, Sefa. "Clustering Frequent Navigation Patterns From Website Logs Using Ontology And Temporal Information." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12613979/index.pdf.

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Given set of web pages labeled with ontological items, the level of similarity between two web pages is measured using the level of similarity between ontological items of pages labeled with. Using similarity measure between two pages, degree of similarity between two sequences of web page visits can be calculated as well. Using clustering algorithms, similar frequent sequences are grouped and representative sequences are selected from these groups. A new sequence is compared with all clusters and it is assigned to most similar one. Representatives of the most similar cluster can be used in several real world cases. They can be used for predicting and prefetching the next page user will visit or for helping the navigation of user in the website. They can also be used to improve the structure of website for easier navigation. In this study the effect of time spent on each web page during the session is analyzed.
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Benkovská, Petra. "Web Usage Mining." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-3950.

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General characteristic of web mining including methodology and procedures incorporated into this term. Relation to other areas (data mining, artificial intelligence, statistics, databases, internet technologies, management etc.) Web usage mining - data sources, data pre-processing, characterization of analytical methods and tools, interpretation of outputs (results), and possible areas of usage including examples. Suggestion of solution method, realization and a concrete example's outputs interpretation while using above mentioned methods of web usage mining.
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Tanasa, Doru. "Fouille de données d'usage du Web : Contributions au prétraitement de logs Web Intersites et à l'extraction des motifs séquentiels avec un faible support." Phd thesis, Université de Nice Sophia-Antipolis, 2005. http://tel.archives-ouvertes.fr/tel-00178870.

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Les quinze dernières années ont été marquées par une croissance exponentielle du domaine du Web tant dans le nombre de sites Web disponibles que dans le nombre d'utilisateurs de ces sites. Cette croissance a généré de très grandes masses de données relatives aux traces d'usage duWeb par les internautes, celles-ci enregistrées dans des fichiers logs Web. De plus, les propriétaires de ces sites ont exprimé le besoin de mieux comprendre leurs visiteurs afin de mieux répondre à leurs attentes. Le Web Usage Mining (WUM), domaine de recherche assez récent, correspond justement au processus d'extraction des connaissances à partir des données (ECD) appliqué aux données d'usage sur le Web. Il comporte trois étapes principales : le prétraitement des données, la découverte des schémas et l'analyse (ou l'interprétation) des résultats. Un processus WUM extrait des patrons de comportement à partir des données d'usage et, éventuellement, à partir d'informations sur le site (structure et contenu) et sur les utilisateurs du site (profils). La quantité des données d'usage à analyser ainsi que leur faible qualité (en particulier l'absence de structuration) sont les principaux problèmes en WUM. Les algorithmes classiques de fouille de données appliqués sur ces données donnent généralement des résultats décevants en termes de pratiques des internautes (par exemple des patrons séquentiels évidents, dénués d'intérêt). Dans cette thèse, nous apportons deux contributions importantes pour un processus WUM, implémentées dans notre bo^³te à outils AxisLogMiner. Nous proposons une méthodologie générale de prétraitement des logs Web et une méthodologie générale divisive avec trois approches (ainsi que des méthodes concrètes associées) pour la découverte des motifs séquentiels ayant un faible support. Notre première contribution concerne le prétraitement des données d'usage Web, domaine encore très peu abordé dans la littérature. L'originalité de la méthodologie de prétraitement proposée consiste dans le fait qu'elle prend en compte l'aspect multi-sites du WUM, indispensable pour appréhender les pratiques des internautes qui naviguent de fa»con transparente, par exemple, sur plusieurs sites Web d'une même organisation. Outre l'intégration des principaux travaux existants sur ce thème, nous proposons dans notre méthodologie quatre étapes distinctes : la fusion des fichiers logs, le nettoyage, la structuration et l'agrégation des données. En particulier, nous proposons plusieurs heuristiques pour le nettoyage des robots Web, des variables agrégées décrivant les sessions et les visites, ainsi que l'enregistrement de ces données dans un modèle relationnel. Plusieurs expérimentations ont été réalisées, montrant que notre méthodologie permet une forte réduction (jusqu'à 10 fois) du nombre des requêtes initiales et offre des logs structurés plus riches pour l'étape suivante de fouille de données. Notre deuxième contribution vise la découverte à partir d'un fichier log prétraité de grande taille, des comportements minoritaires correspondant à des motifs séquentiels de très faible support. Pour cela, nous proposons une méthodologie générale visant à diviser le fichier log prétraité en sous-logs, se déclinant selon trois approches d'extraction de motifs séquentiels au support faible (Séquentielle, Itérative et Hiérarchique). Celles-ci ont été implémentées dans des méthodes concrètes hybrides mettant en jeu des algorithmes de classification et d'extraction de motifs séquentiels. Plusieurs expérimentations, réalisées sur des logs issus de sites académiques, nous ont permis de découvrir des motifs séquentiels intéressants ayant un support très faible, dont la découverte par un algorithme classique de type Apriori était impossible. Enfin, nous proposons une boite à outils appelée AxisLogMiner, qui supporte notre méthodologie de prétraitement et, actuellement, deux méthodes concrètes hybrides pour la découverte des motifs séquentiels en WUM. Cette boite à outils a donné lieu à de nombreux prétraitements de fichiers logs et aussi à des expérimentations avec nos méthodes implémentées.
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Ngok, Man Chan. "Log mining to support web query expansions." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1783608.

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Leibold, Markus. "Web Log Mining als Controllinginstrument der PR." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11675715.

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Oosthuizen, Craig Peter. "Web usage mining of organisational web sites." Thesis, Nelson Mandela Metropolitan University, 2005. http://hdl.handle.net/10948/399.

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Web Usage Mining (WUM) can be used to determine whether the information architecture of a web site is structured correctly. Existing WUM tools however, do not indicate which web usage mining algorithms are used or provide effective graphical visualisations of the results obtained. WUM techniques can be used to determine typical navigation patterns of the users of organisational web sites. An organisational web site can be described as a site which has a high level of content. The Computer Science & Information Systems (CS&IS) web site at the Nelson Mandela Metropolitan University (NMMU) is an example of such a web site. The process of combining WUM and information visualisation techniques in order to discover useful information about web usage patterns is called visual web mining. The goal of this research is to discuss the development of a WUM model and a prototype, called WebPatterns, which allows the user to effectively visualise web usage patterns of an organisational web site. This will facilitate determining whether the information architecture of the CS&IS web site is structured correctly. The WUM algorithms used in WebPatterns are association rule mining and sequence analysis. The purpose of association rule mining is to discover relationships between different web pages within a web site. Sequence analysis is used to determine the longest time ordered paths that satisfy a user specified minimum frequency. A radial tree layout is used in WebPatterns to visualise the static structure of the organisational web site. The structure of the web site is laid out radially, with the home page in the middle and other pages positioned in circles at various levels around it. Colour and other visual cues are used to show the results of the WUM algorithms. User testing was used to determine the effectiveness and usefulness of WebPatterns for visualising web usage patterns. The results of the user testing clearly show that the participants were highly satisfied with the visual design and information provided by WebPatterns. All the participants also indicated that they would like to use WebPatterns in the future. Analysis of the web usage patterns presented by WebPatterns was used to determine that the information architecture of the CS&IS web site can be restructured to better facilitate information retrieval. Changes to the CS&IS web site web were suggested, included placing embedded hyperlinks on the home page to the frequently accessed sections of the web site.
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Books on the topic "Web usage log mining"

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WEBKDD 2001 (2001 San Francisco, Calif.). WEBKDD 2001--mining web log data across all customer touch points: Third international workshop, San Francisco, CA, USA, August 26, 2001 : revised papers. Berlin: Springer, 2002.

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Ron, Kohavi, ed. WEBKDD 2001: Mining web log data across all customers touch points : third International Workshop, San Francisco, CA, USA, August 26, 2001 : revised papers. Berlin: Springer, 2002.

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Nasraoui, Olfa, Osmar Zaïane, Myra Spiliopoulou, Bamshad Mobasher, Brij Masand, and Philip S. Yu, eds. Advances in Web Mining and Web Usage Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321.

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Mobasher, Bamshad, Olfa Nasraoui, Bing Liu, and Brij Masand, eds. Advances in Web Mining and Web Usage Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11899402.

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Nasraoui, Olfa, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, and Brij Masand, eds. Advances in Web Mining and Web Usage Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-77485-3.

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Zhang, Haizheng, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, and John Yen, eds. Advances in Web Mining and Web Usage Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00528-2.

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Bensberg, Frank. Web Log Mining als Instrument der Marketingforschung. Wiesbaden: Deutscher Universitätsverlag, 2001. http://dx.doi.org/10.1007/978-3-322-91505-4.

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Web data mining: Exploring hyperlinks, contents, and usage data. 2nd ed. Heidelberg: Springer, 2011.

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Abraham, Kandel, ed. Search engines, link analysis, and user's Web behavior: [a unifying Web mining approach]. Berlin: Springer, 2008.

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J, Jansen Bernard, ed. Web search: Public searching on the Web. Dorndrecht, Netherlands: Kluwer Academic Publishers, 2004.

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Book chapters on the topic "Web usage log mining"

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Punin, John R., Mukkai S. Krishnamoorthy, and Mohammed J. Zaki. "LOGML: Log Markup Language for Web Usage Mining." In WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points, 88–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45640-6_5.

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Kolekar, Sucheta V., Sriram G. Sanjeevi, and D. S. Bormane. "Acquisition of User’s Learning Styles Using Log Mining Analysis through Web Usage Mining Process." In Intelligent Decision Technologies, 809–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22194-1_80.

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Berendt, Bettina. "Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences." In WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points, 1–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45640-6_1.

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Shahabi, Cyrus, and Farnoush Banaei-Kashani. "A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking." In WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points, 113–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45640-6_6.

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Lu, Zhiyong, Yiyu Yao, and Ning Zhong. "Web Log Mining." In Web Intelligence, 173–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05320-1_9.

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Xu, Guandong, Yanchun Zhang, and Lin Li. "Web Usage Mining." In Web Mining and Social Networking, 109–42. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7735-9_6.

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Román, Pablo E., Gastón L’Huillier, and Juan D. Velásquez. "Web Usage Mining." In Advanced Techniques in Web Intelligence - I, 143–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14461-5_6.

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Liu, Bing, Bamshad Mobasher, and Olfa Nasraoui. "Web Usage Mining." In Web Data Mining, 527–603. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_12.

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Rong, Zhen, Yan Tang, and Su Liu. "Research on Web Log Mining." In Lecture Notes in Electrical Engineering, 849–56. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4850-0_108.

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Anand, Sarabjot Singh, Maurice Mulvenna, and Karine Chevalier. "On the Deployment of Web Usage Mining." In Web Mining: From Web to Semantic Web, 23–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30123-3_2.

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Conference papers on the topic "Web usage log mining"

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Aye, Theint Theint. "Web log cleaning for mining of web usage patterns." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5764181.

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Hussain, Tasawar, Sohail Asghar, and Nayyer Masood. "Web usage mining: A survey on preprocessing of web log file." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625730.

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Gashaw, Yonas, and Fang Liu. "Performance evaluation of frequent pattern mining algorithms using web log data for web usage mining." In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8302317.

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Zhang, Yu, Li Dai, and Zhi-Jie Zhou. "A New Perspective of Web Usage Mining: Using Enterprise Proxy Log." In 2010 International Conference on Web Information Systems and Mining (WISM). IEEE, 2010. http://dx.doi.org/10.1109/wism.2010.20.

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Khasawneh, Natheer, and Chien-chung Chan. "Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining." In 2006 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE, 2006. http://dx.doi.org/10.1109/wi.2006.32.

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Navarro-Arribas, Guillermo, and Vicenc Torra. "Towards microaggregation of log files for Web usage mining in B2C e-commerce." In NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2009. http://dx.doi.org/10.1109/nafips.2009.5156452.

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Goel, Neha, and C. K. Jha. "Preprocessing web logs: A critical phase in web usage mining." In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, 2015. http://dx.doi.org/10.1109/icacea.2015.7164776.

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Eltahir, Mirghani A., and Anour F. A. Dafa-Alla. "Extracting knowledge from web server logs using web usage mining." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6633973.

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Malik, S. K., N. Prakash, and S. A. M. Rizvi. "Ontology and Web Usage Mining towards an Intelligent Web Focusing Web Logs." In 2010 International Conference on Computational Intelligence and Communication Networks (CICN 2010). IEEE, 2010. http://dx.doi.org/10.1109/cicn.2010.90.

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Anitha, V., and P. Isakki. "A survey on predicting user behavior based on web server log files in a web usage mining." In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE). IEEE, 2016. http://dx.doi.org/10.1109/icctide.2016.7725340.

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