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Journal articles on the topic 'Usage profile Web usage mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Zhang, Junwen, Qunqun Xue, and Dalin Zhang. "Mining usage profile to improve web performance testing." International Journal of Services Operations and Informatics 9, no. 3 (2018): 189. http://dx.doi.org/10.1504/ijsoi.2018.094653.

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Zhang, Junwen, Dalin Zhang, and Qunqun Xue. "Mining usage profile to improve web performance testing." International Journal of Services Operations and Informatics 9, no. 3 (2018): 189. http://dx.doi.org/10.1504/ijsoi.2018.10015974.

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3

Rathipriya, R., K. Thangavel, and J. Bagyamani. "Usage Profile Generation from Web Usage Data Using Hybrid Biclustering Algorithm." International Journal of Applied Evolutionary Computation 2, no. 4 (2011): 37–49. http://dx.doi.org/10.4018/jaec.2011100103.

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Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by using a biclustering algorithm. This method automatically identifies the groups of users that show similar browsing patterns under a specific subset of the pages. In this paper, mutation operator from Genetic Algorithms is incorporated into the Binary Particle Swarm Optimization (BPSO) for biclustering of web usage data. This hybridization can increase the diversity

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Ms.Dipa, Dixit, and Gadge Jayant. "Automatic Recommendation for Online Users Using Web Usage Mining." International Journal of Managing Information Technology (IJMIT) 2, no. 3 (2010): 33 to 42. https://doi.org/10.5281/zenodo.3357685.

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A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day,  thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be

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Dipa, Dixit, and Gadge Jayant. "Automatic Recommendation for Online Users Using Web Usage Mining." International Journal of Managing Information Technology (IJMIT) 2, no. 3 (2014): 33 to 42. https://doi.org/10.5281/zenodo.3593612.

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A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used

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John, Joan M., G. Venifa Mini, and E. Arun. "User Profile Tracking by Web Usage Mining in Cloud Computing." Procedia Engineering 38 (2012): 3270–77. http://dx.doi.org/10.1016/j.proeng.2012.06.378.

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Nasraoui, O., M. Soliman, E. Saka, A. Badia, and R. Germain. "A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites." IEEE Transactions on Knowledge and Data Engineering 20, no. 2 (2008): 202–15. http://dx.doi.org/10.1109/tkde.2007.190667.

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An, Kye-Sun, Se-Jin Go, Jun Jiong, and Phill-Kyu Rhee. "Generator of Dynamic User Profiles Based on Web Usage Mining." KIPS Transactions:PartB 9B, no. 4 (2002): 389–90. http://dx.doi.org/10.3745/kipstb.2002.9b.4.389.

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Mallik, Moksud Alam, and Nurul Fariza Zulkurnain. "An Efficient Fuzzy Clustering Algorithm for Mining User Session Clusters on Web Log Data." International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) 2, no. 2 (2022): 80–93. http://dx.doi.org/10.34010/injiiscom.v2i2.7349.

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Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web Usage Mining gives the organizations the capacity to deliver results more compelling to their organizations and expanding of sales. Client access patterns can be mined from web access log information using Web Usage Mining (WUM) techniques. Because there are so many end-user sessions and URL resourc

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Jenifer, Mahilraj. "Trajectory Based Location Prediction and Enriched Ontological User Profiles for Efficient Website Recommendation." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 250–57. https://doi.org/10.35940/ijrte.C4363.099320.

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The spread over of huge amount of information in the vast area of internet makes difficult for the users to obtain the search items that are relevant to them. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfill their need, it is necessary to know their preferences of search at various contexts. In general, the user profiles are used to determine the taste of the users. The traditional method of user profiling does not provide a complete detail regarding their search. In addition, the search preference of the individuals var

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Abdurrahman Abdurrahman, Riyanto BT, Govindaraju R, and Mandala R. "cANT WUM : Web User Classification using Ant Colony Optimization Algorithm." International Journal of Information Technology and Business 6, no. 2 (2024): 11–16. https://doi.org/10.24246/ijiteb.622024.11-16.

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Web Usage Mining (WUM) is the use of data mining methods to extract knowledge from web usage data. One function of WUM is to support Business Intelligence (BI) purpose in which one of the important information needed is the classification of web users that can be used for acquisition, penetration, and user retention activity. There are two main problems encountered in conducting the classification of web users. The first is the determination of antecedent attributes as a term of classification rules, which is a major problem in data mining classification function in general. The second problem

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Jameel, Ambareen, and Mohd Usman Khan. "Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 31–37. http://dx.doi.org/10.55524/ijircst.2024.12.4.5.

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In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user

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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 (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

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NASRAOUI, OLFA, and RAGHU KRISHNAPURAM. "AN EVOLUTIONARY APPROACH TO MINING ROBUST MULTI-RESOLUTION WEB PROFILES AND CONTEXT SENSITIVE URL ASSOCIATIONS." International Journal of Computational Intelligence and Applications 02, no. 03 (2002): 339–48. http://dx.doi.org/10.1142/s1469026802000646.

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We present a technique for simultaneously mining Web navigation patterns and maximally frequent context-sensitive itemsets (URL associations) from the historic user access data stored in Web server logs. A new hierarchical clustering technique that exploits the symbiosis between clusters in feature space and genetic biological niches in nature, called Hierarchical Unsupervised Niche Clustering (H-UNC) is presented. We use H-UNC as part of a complete system of knowledge discovery in Web usage data. Our approach does not necessitate fixing the number of clusters in advance, is insensitive to ini

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Hussein, Wedad, Rasha Ismail, Tarek Gharib, and Mostafa G. M. Mostafa. "A Personalized Recommender System Based on a Hybrid Model." JUCS - Journal of Universal Computer Science 19, no. (15) (2013): 2224–40. https://doi.org/10.3217/jucs-019-15-2224.

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Recommender systems are means for web personalization and tailoring the browsing experience to the users' specific needs. There are two categories of recommender systems; memory-based and model-based systems. In this paper we propose a personalized recommender system for the next page prediction that is based on a hybrid model from both categories. The generalized patterns generated by a model based techniques are tailored to specific users by integrating user profiles generated from the traditional memory-based system's user-item matrix. The suggested system offered a significant improvement

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Xu, Beijie, Mimi Recker, Xiaojun Qi, Nicholas Flann, and Lei Ye. "Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms." Journal of Educational Data Mining 5, no. 2 (2013): 38–68. https://doi.org/10.5281/zenodo.3554634.

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This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data sources, three types of comparisons of resulting clusters are presented: 1) Davies-Bouldin indices, 2) clustering results validated with user profile data, and 3) cluster evolution. Latent Class Analysis is superior to K-means on all three comparisons.

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Librado, Dison, and Wagito Wagito. "PEMETAAN AKSES HALAMAN SITUS WEB BERBASIS LOG-ACCESS." Jurnal SAINTEKOM 9, no. 2 (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. I

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El Allioui, Youssouf. "Advanced prediction of learner's profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm." International Journal of Computer Aided Engineering and Technology 11, no. 4/5 (2019): 495. http://dx.doi.org/10.1504/ijcaet.2019.10020286.

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Allioui, Youssouf El. "Advanced prediction of learner's profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm." International Journal of Computer Aided Engineering and Technology 11, no. 4/5 (2019): 495. http://dx.doi.org/10.1504/ijcaet.2019.100447.

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Kolekar, Sucheta V., Radhika M. Pai, and Manohara Pai M M. "Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System." International Journal of Emerging Technologies in Learning (iJET) 12, no. 06 (2017): 31. http://dx.doi.org/10.3991/ijet.v12i06.6579.

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The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM)

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21

M, Chandan. "Predicting the Students Performance Using WUM and Machine Learning Technique." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1267–69. http://dx.doi.org/10.22214/ijraset.2024.60649.

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Abstract: This project aims to develop a predictive model for assessing students’ academic performance by integrating web usage mining (WUM) and machine learning (ML) techniques. The model leverages user-provided data such as gender, parental profession, reading score, and writing score, along with behavioural insights from online educational platform interactions. The methodology includes data preprocessing, feature extraction, and adaptive algorithm selection to enhance prediction accuracy and adaptability across various student profiles. The integration of WUM provides a robust framework fo

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Petrič, Karl, Teodor Petrič, Marjan Krisper, and Vladislav Rajkovič. "User Profiling on a Pilot Digital Library with the Final Result of a New Adaptive Knowledge Management Solution." Knowledge Organization 38, no. 2 (2011): 96–113. https://doi.org/10.5771/0943-7444-2011-2-96.

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<strong>Abstract</strong> In this article, several procedures (e.g., measurements, information retrieval analyses, power law, association rules, hierarchical clustering) are introduced which were made on a pilot digital library. Information retrievals of web users from 01/01/2003 to 01/01/2006 on the internal search engine of the pilot digital library have been analyzed. With the power law method of data processing, a constant information retrieval pattern has been established, stable over a longer period of time. After this, the data have been analyzed. On the basis of the accomplished measur

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23

Alonso-Secades, Vidal, Alfonso-José López-Rivero, Manuel Martín-Merino-Acera, Manuel-José Ruiz-García, and Olga Arranz-García. "Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries." Electronics 11, no. 9 (2022): 1487. http://dx.doi.org/10.3390/electronics11091487.

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Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on pr

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Alonso, Secades Vidal, Rivero Alfonso López, Acera Manuel Martín-Merino, García Manuel José Ruiz, and García Olga Arranz. "Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries." Electronics 11, no. 9 (2022): 1487. https://doi.org/10.3390/electronics11091487.

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Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on pr

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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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Ahmed, Moiz Uddin, and Amjad Mahmood. "Web Usage Mining." International Journal of Technology Diffusion 3, no. 3 (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 org

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

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Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to usersâ€™ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also p

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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 (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 activi

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Pooja, Pooja. "Web Usage Mining: An Approach." International Journal of Computer Applications 86, no. 12 (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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Dr., Madan Lal Bhasin. "Online Privacy Protection: Privacy Seals, Government Regulations and Technological Solutions." International Journal of Management Sciences and Business Research 5, no. 7 (2016): 96–116. https://doi.org/10.5281/zenodo.3464813.

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The state of privacy in the 21st century is a worldwide concern, given the Internet‘s global reach. The privacy violation on the internet is a significant problem and internet users have a right to adequate privacy. New e-business technologies have increased the ability of online merchants to collect, monitor, target, profile, and even sell personal information about consumers to third parties. Governments, business houses and employers collect data and monitor people, but their practices often threaten an individual‘s privacy. Because vast amount of data can be collected on the In

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Khatoon, Asfiya, and Kuldeep Jaiswal. "Web Page Ranking using Web Usage Mining." IJARCCE 6, no. 4 (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 (2000): 127–34. http://dx.doi.org/10.1145/345124.345167.

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Nijat Babayev, Nijat Babayev. "WEB MINING: DATA MINING ON THE INTERNET." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 23, no. 12 (2022): 182–93. http://dx.doi.org/10.36962/pahtei23122022-182.

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In the article“Web Mining: Data Mining on the Web” 3 different components of Web Mining have been described and mainly Web Usage Mining has been discussed in detail and analyzed with examples. The general theme of the article is clarified by giving such sub-topics: Difficulties in analyzing data from the Internet, Stages of Web-Mining, Analysis of the use of Web resources (Web Usage Mining), Web Server Log Files and etc. General Relationships Between Web Mining Categories and Data Mining Tasks are shown. At the end, in the Conclusion, it is shown that to solve such problems, Data Mining techno

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HOGO, MOFREH, MIROSLAV SNOREK, and PAWAN LINGRAS. "TEMPORAL VERSUS LATEST SNAPSHOT WEB USAGE MINING USING KOHONEN SOM AND MODIFIED KOHONEN SOM BASED ON THE PROPERTIES OF ROUGH SETS THEORY." International Journal on Artificial Intelligence Tools 13, no. 03 (2004): 569–91. http://dx.doi.org/10.1142/s0218213004001697.

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Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal usage patterns, which describe the temporal behavior of users on the Internet Web site, to understand the temporal users' behavior during different time slices. Clustering and classification are two important functions in Web mining. Classes, and associations in Web mining do not necessarily have crisp boundaries. Therefore the conventional clustering techniques became unsuitable to find such clusters and associations, where these conventional classification algorithms provi

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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 (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

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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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Thiyagarajan, V. S. "Web Data mining-A Research area in Web usage mining." IOSR Journal of Computer Engineering 13, no. 1 (2013): 22–26. http://dx.doi.org/10.9790/0661-1312226.

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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 (2014): 99–103. http://dx.doi.org/10.14445/22312803/ijctt-v12p119.

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Abraham, Ajith. "Business Intelligence from Web Usage Mining." Journal of Information & Knowledge Management 02, no. 04 (2003): 375–90. http://dx.doi.org/10.1142/s0219649203000565.

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The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personal

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Dimitrijevic, Maja, and Zita Bošnjak. "Web Usage Association Rule Mining System." Interdisciplinary Journal of Information, Knowledge, and Management 6 (2011): 137–50. http://dx.doi.org/10.28945/1372.

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Gupta, Adarsh, Mukul Atawnia, Rohan Wadhwa, Shreya Mahar, and Vinita Rohilla. "Comparative Analysis of Web Usage Mining." IJARCCE 6, no. 4 (2017): 324–28. http://dx.doi.org/10.17148/ijarcce.2017.6461.

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Jaree Thongkam, and Vatinee Sukmak. "Web Usage Mining Techniques and Applications." International Journal of Advancements in Computing Technology 4, no. 20 (2012): 633–41. http://dx.doi.org/10.4156/ijact.vol4.issue20.73.

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Waqas, Muhammad, Maria Iram, Sara Shahzad, Sidra Arshad, and Tahir Nawaz. "Knowledge Extraction Using Web Usage Mining." ICST Transactions on Scalable Information Systems 5, no. 16 (2018): 154551. http://dx.doi.org/10.4108/eai.13-4-2018.154551.

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Panchal, Nirali H., and Ompriya Kale. "A Survey on Web Usage Mining." International Journal of Computer Trends and Technology 17, no. 4 (2014): 177–81. http://dx.doi.org/10.14445/22312803/ijctt-v17p134.

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Hong, Tzung-Pei, Cheng-Ming Huang, and Shi-Jinn Horng. "Linguistic object-oriented web-usage mining." International Journal of Approximate Reasoning 48, no. 1 (2008): 47–61. http://dx.doi.org/10.1016/j.ijar.2007.06.006.

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Grossmann, Wilfried, Marcus Hudec, and Roland Kurzawa. "Web usage mining in e-commerce." International Journal of Electronic Business 2, no. 5 (2004): 480. http://dx.doi.org/10.1504/ijeb.2004.005881.

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Suguna, R., and D. Sharmila. "An Overview of Web Usage Mining." International Journal of Computer Applications 39, no. 13 (2012): 11–13. http://dx.doi.org/10.5120/4879-7314.

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J., Umarani, and S. Manikandan Dr. "PATTERN DISCOVERY TECHNIQUES IN WEB USAGE MINING." International Journal of Scientific Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332044.

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WWW is a very popular and interactive medium for broadcasting information today. Due to the vast, diverse and lively nature of web it advancesthe scalability, multimedia data and temporal issues respectively. The development of the web has given rise to large quantity of data that is freely available for user access.Web Usage Mining enhances the user experience while browsing web pages by using past history of web data. It also used to improve the web site navigation. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web usage mining is

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