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Статті в журналах з теми "Web usage data mining techniques"
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 (August 1, 2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.
Повний текст джерелаHarika, 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.
Повний текст джерелаV, Sathiyamoorthi, and Murali Bhaskaran .V. "DATA PREPARATION TECHNIQUES FOR WEB USAGE MINING IN WORLD WIDE WEB." International Journal on Information Sciences and Computing 4, no. 1 (2010): 55–60. http://dx.doi.org/10.18000/ijisac.50067.
Повний текст джерелаYau, 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.
Повний текст джерелаHOGO, 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 (September 2004): 569–91. http://dx.doi.org/10.1142/s0218213004001697.
Повний текст джерелаEzzikouri, 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.
Повний текст джерелаDE, SUPRIYA KUMAR, and P. RADHA KRISHNA. "MINING WEB DATA USING CLUSTERING TECHNIQUE FOR WEB PERSONALIZATION." International Journal of Computational Intelligence and Applications 02, no. 03 (September 2002): 255–65. http://dx.doi.org/10.1142/s1469026802000580.
Повний текст джерелаRamanathaiah, 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.
Повний текст джерелаHamodi, Yaser Issam, Ruaa Riyadh Hussein, and Naeem Th Yousir. "Development of a Unifying Theory for Data Mining Using Clustering Techniques." Webology 17, no. 2 (December 21, 2020): 01–14. http://dx.doi.org/10.14704/web/v17i2/web17012.
Повний текст джерелаAbraham, Ajith. "Business Intelligence from Web Usage Mining." Journal of Information & Knowledge Management 02, no. 04 (December 2003): 375–90. http://dx.doi.org/10.1142/s0219649203000565.
Повний текст джерелаДисертації з теми "Web usage data mining techniques"
Khalil, Faten. "Combining web data mining techniques for web page access prediction." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00004341/.
Повний текст джерелаNagi, Mohamad. "Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14200.
Повний текст джерелаKhasawneh, Natheer Yousef. "Toward Better Website Usage: Leveraging Data Mining Techniques and Rough Set Learning to Construct Better-to-use Websites." Akron, OH : University of Akron, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1120534472.
Повний текст джерела"August, 2005." Title from electronic dissertation title page (viewed 01/14/2006) Advisor, John Durkin; Committee members, John Welch, James Grover, Yueh-Jaw Lin, Yingcai Xiao, Chien-Chung Chan; Department Chair, Alex Jose De Abreu-Garcia; Dean of the College, George Haritos; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.
Ammari, Ahmad N. "Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns : the development and evaluation of new Web mining methods that enhance information retrieval and improve the understanding of users' Web behavior in websites and social blogs." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5269.
Повний текст джерелаAmmari, Ahmad N. "Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5269.
Повний текст джерелаNorguet, Jean-Pierre. "Semantic analysis in web usage mining." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210890.
Повний текст джерелаIndeed, according to organizations theory, the higher levels in the organizations need summarized and conceptual information to take fast, high-level, and effective decisions. For Web sites, these levels include the organization managers and the Web site chief editors. At these levels, the results produced by Web analytics tools are mostly useless. Indeed, most of these results target Web designers and Web developers. Summary reports like the number of visitors and the number of page views can be of some interest to the organization manager but these results are poor. Finally, page-group and directory hits give the Web site chief editor conceptual results, but these are limited by several problems like page synonymy (several pages contain the same topic), page polysemy (a page contains several topics), page temporality, and page volatility.
Web usage mining research projects on their part have mostly left aside Web analytics and its limitations and have focused on other research paths. Examples of these paths are usage pattern analysis, personalization, system improvement, site structure modification, marketing business intelligence, and usage characterization. A potential contribution to Web analytics can be found in research about reverse clustering analysis, a technique based on self-organizing feature maps. This technique integrates Web usage mining and Web content mining in order to rank the Web site pages according to an original popularity score. However, the algorithm is not scalable and does not answer the page-polysemy, page-synonymy, page-temporality, and page-volatility problems. As a consequence, these approaches fail at delivering summarized and conceptual results.
An interesting attempt to obtain such results has been the Information Scent algorithm, which produces a list of term vectors representing the visitors' needs. These vectors provide a semantic representation of the visitors' needs and can be easily interpreted. Unfortunately, the results suffer from term polysemy and term synonymy, are visit-centric rather than site-centric, and are not scalable to produce. Finally, according to a recent survey, no Web usage mining research project has proposed a satisfying solution to provide site-wide summarized and conceptual audience metrics.
In this dissertation, we present our solution to answer the need for summarized and conceptual audience metrics in Web analytics. We first described several methods for mining the Web pages output by Web servers. These methods include content journaling, script parsing, server monitoring, network monitoring, and client-side mining. These techniques can be used alone or in combination to mine the Web pages output by any Web site. Then, the occurrences of taxonomy terms in these pages can be aggregated to provide concept-based audience metrics. To evaluate the results, we implement a prototype and run a number of test cases with real Web sites.
According to the first experiments with our prototype and SQL Server OLAP Analysis Service, concept-based metrics prove extremely summarized and much more intuitive than page-based metrics. As a consequence, concept-based metrics can be exploited at higher levels in the organization. For example, organization managers can redefine the organization strategy according to the visitors' interests. Concept-based metrics also give an intuitive view of the messages delivered through the Web site and allow to adapt the Web site communication to the organization objectives. The Web site chief editor on his part can interpret the metrics to redefine the publishing orders and redefine the sub-editors' writing tasks. As decisions at higher levels in the organization should be more effective, concept-based metrics should significantly contribute to Web usage mining and Web analytics.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
Sobolewska, Katarzyna-Ewa. "Web links utility assessment using data mining techniques." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2936.
Повний текст джерелаakasha.kate@gmail.com
Bayir, Murat Ali. "A New Reactive Method For Processing Web Usage Data." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12607323/index.pdf.
Повний текст джерелаSmart-SRA'
is introduced. Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigations of Web users. As in classical data mining, data processing and pattern discovery are the main issues in web usage mining. The first phase of the web usage mining is the data processing phase including session reconstruction. Session reconstruction is the most important task of web usage mining since it directly affects the quality of the extracted frequent patterns at the final step, significantly. Session reconstruction methods can be classified into two categories, namely '
reactive'
and '
proactive'
with respect to the data source and the data processing time. If the user requests are processed after the server handles them, this technique is called as &lsquo
reactive&rsquo
, while in &lsquo
proactive&rsquo
strategies this processing occurs during the interactive browsing of the web site. Smart-SRA is a reactive session reconstruction techique, which uses web log data and the site topology. In order to compare Smart-SRA with previous reactive methods, a web agent simulator has been developed. Our agent simulator models behavior of web users and generates web user navigations as well as the log data kept by the web server. In this way, the actual user sessions will be known and the successes of different techniques can be compared. In this thesis, it is shown that the sessions generated by Smart-SRA are more accurate than the sessions constructed by previous heuristics.
Wu, Hao-cun, and 吳浩存. "A multidimensional data model for monitoring web usage and optimizing website topology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29528215.
Повний текст джерелаÖzakar, Belgin Püskülcü Halis. "Finding And Evaluating Patterns In Wes Repository Using Database Technology And Data Mining Algorithms/." [s.l.]: [s.n.], 2002. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000130.pdf.
Повний текст джерелаКниги з теми "Web usage data mining techniques"
Web data mining: Exploring hyperlinks, contents, and usage data. 2nd ed. Heidelberg: Springer, 2011.
Знайти повний текст джерелаauthor, Roghani Ali, ed. Big data analytics for beginners. [India]: Crux Tech Limited, 2014.
Знайти повний текст джерелаTaniar, David, and Lukman Hakim Iwan. Exploring advances in interdisciplinary data mining and analytics: New trends. Hershey, PA: Information Science Reference, 2012.
Знайти повний текст джерелаDēta mainingu to shūgōchi: Kiso kara web, sōsharu media made = Data mining and collective intelligence from basics to web and social media. Tōkyō: Kyōritsu Shuppan, 2012.
Знайти повний текст джерелаdo, Prado Hercules Antonio, and Ferneda Edilson, eds. Emerging technologies of text mining: Techniques and applications. Hershey PA: Information Science Reference, 2007.
Знайти повний текст джерелаservice), ScienceDirect (Online, ed. Cult of analytics: Driving online marketing strategies using Web analytics. Amsterdam: Elsevier/Butterworth-Heinemann, 2009.
Знайти повний текст джерелаZaïane, Osmar R., Jaideep Srivastava, Myra Spiliopoulou, and Brij Masand, eds. WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11784.
Повний текст джерелаAbraham, Kandel, ed. Search engines, link analysis, and user's Web behavior: [a unifying Web mining approach]. Berlin: Springer, 2008.
Знайти повний текст джерела1968-, Nasraoui Olfa, ed. Advances in web mining and web usage analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006 : revised papers. Berlin: Springer, 2007.
Знайти повний текст джерелаDevelopments in data extraction, management, and analysis. Hershey, PA: Information Science Reference, 2012.
Знайти повний текст джерелаЧастини книг з теми "Web usage data mining techniques"
Aggarwal, Charu C., and Philip S. Yu. "On Clustering Techniques for Change Diagnosis in Data Streams." In Advances in Web Mining and Web Usage Analysis, 139–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321_8.
Повний текст джерелаde Paiva, Fábio A. Procópio, and José Alfredo F. Costa. "Using SOM to Clustering of Web Sessions Extracted by Techniques of Web Usage Mining." In Intelligent Data Engineering and Automated Learning - IDEAL 2012, 484–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_59.
Повний текст джерелаLiu, 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.
Повний текст джерелаRomán, 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.
Повний текст джерелаCastellano, Giovanna, Anna M. Fanelli, and Maria A. Torsello. "Web Usage Mining: Discovering Usage Patterns for Web Applications." In Advanced Techniques in Web Intelligence-2, 75–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33326-2_4.
Повний текст джерелаda Silva, Alzennyr, Yves Lechevallier, Fabrice Rossi, and Francisco de Carvalho. "Clustering Dynamic Web Usage Data." In Innovative Applications in Data Mining, 71–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88045-5_4.
Повний текст джерелаRossi, Fabrice, Francisco De Carvalho, Yves Lechevallier, and Alzennyr Da Silva. "Dissimilarities for Web Usage Mining." In Studies in Classification, Data Analysis, and Knowledge Organization, 39–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34416-0_5.
Повний текст джерелаSaidi, Samia, and Yahya Slimani. "Enhancing Web Caching Using Web Usage Mining Techniques." In Communications in Computer and Information Science, 425–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14171-3_38.
Повний текст джерелаL’Huillier, Gaston, and Juan D. Velásquez. "Web Usage Data Pre-processing." In Advanced Techniques in Web Intelligence-2, 11–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33326-2_2.
Повний текст джерелаLi, Xiang-ying. "Data Preprocessing in Web Usage Mining." In The 19th International Conference on Industrial Engineering and Engineering Management, 257–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38391-5_27.
Повний текст джерелаТези доповідей конференцій з теми "Web usage data mining techniques"
Nassar, Omer Adel, and Nedhal A. Al Saiyd. "The integrating between web usage mining and data mining techniques." In 2013 5th International Conference on Computer Science and Information Technology (CSIT). IEEE, 2013. http://dx.doi.org/10.1109/csit.2013.6588787.
Повний текст джерелаSukumar, P., L. Robert, and S. Yuvaraj. "Review on modern Data Preprocessing techniques in Web usage mining (WUM)." In 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2016. http://dx.doi.org/10.1109/csitss.2016.7779441.
Повний текст джерелаLiu, Fei. "Research on data preparation technique in web usage mining." In 2013 International Conference of Information Technology and Industrial Engineering. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/itie131492.
Повний текст джерелаBraz, Fernando J. "Knowledge Discovery on Trajectory Data Warehouses: Possible usage of the Data Mining Techniques." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2008. http://dx.doi.org/10.5753/sbsi.2008.5921.
Повний текст джерелаShivaprasad, G., N. V. Subbareddy, U. Dinesh Acharya, R. B. Patel, and B. P. Singh. "Knowledge Discovery from Web Usage Data: Research and Development of Web Access Pattern Tree Based Sequential Pattern Mining Techniques: A Survey." In INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526223.
Повний текст джерелаBahri, Maroua, Albert Bifet, Silviu Maniu, and Heitor Murilo Gomes. "Survey on Feature Transformation Techniques for Data Streams." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/668.
Повний текст джерелаYing Wah, The, Mustaffa Kamal Nor, Zaitun Abu Bakar, and Lee Sai Peck. "Data Mining in Computer Auditing." In 2002 Informing Science + IT Education Conference. Informing Science Institute, 2002. http://dx.doi.org/10.28945/2586.
Повний текст джерелаN. Agu, Monica, Stephen Nabareseh, and Christian Nedu Osakwe. "Investigating Web Based Marketing in the Context of Micro and Small-Scale Enterprises (MSEs): A Decision Tree Classification Technique." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2201.
Повний текст джерелаMadiraju, Praveen, and Yanqing Zhang. "Web usage data mining agent." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460231.
Повний текст джерелаAlbanese, Massimiliano, Antonio Picariello, Carlo Sansone, and Lucio Sansone. "A web personalization system based on web usage mining techniques." In the 13th international World Wide Web conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1013367.1013439.
Повний текст джерела