Relevant bibliographies by topics / Web usage mining. Data mining. World Wide Web

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

Academic literature on the topic 'Web usage mining. Data mining. World Wide Web'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Web usage mining. Data mining. World Wide Web"

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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 users’ 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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V, 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.

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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 (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 minin
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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 (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
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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 (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 algorithm
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Chandrashaker Reddy, P., and A. Suresh Babu. "Usage of co-event pattern mining with optimal fuzzy rule-based classifier for effective web page retrieval." International Journal of Engineering & Technology 7, no. 3.29 (2018): 275. http://dx.doi.org/10.14419/ijet.v7i3.29.18811.

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With the coming of the World Wide Web and the rise of web-based business applications and informal organizations, associations over the web create a lot of information on a daily basis. It is becoming more complex and critical task to retrieve exact information from web expected by its users. In the recent times, the Web has extended its noteworthiness to the point of transforming into the point of convergence of our propelled lives. The search engine as an apparatus to explore the web must get the coveted outcomes for any given query. The greater part of the search engines can't totally fulfi
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Shirgave, Suresh, Prakash Kulkarni, and José Borges. "Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions." International Journal of Information Technology & Decision Making 13, no. 04 (2014): 721–53. http://dx.doi.org/10.1142/s0219622014500643.

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The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show th
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Idrizi, Bashkim, and Mirdon Kurteshi. "Web System for Online and Onsite Usage of Geoinformation by Surveying Sector in Kosovo. Case Study: Ferizaj Municipality." Geosfera Indonesia 4, no. 3 (2019): 230. http://dx.doi.org/10.19184/geosi.v4i3.13469.

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The purpose of research to determine and contribute in more efficient services to geoinformation stakeholders, as well as to give positive impact on increasing income in geo business sector, voluntary based web system for online usage of geoinformation in Kosovo has been developed. The method used was puting in to one place many sourcec via WMS and WFS services, by creating thematic SDI, in order to have online system with dynamic data comming from official databases with update from last day on 5 pm. System is open for usage by all interested parts, however official registration is required.
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Cooley, Robert, Bamshad Mobasher, and Jaideep Srivastava. "Data Preparation for Mining World Wide Web Browsing Patterns." Knowledge and Information Systems 1, no. 1 (1999): 5–32. http://dx.doi.org/10.1007/bf03325089.

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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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Dissertations / Theses on the topic "Web usage mining. Data mining. World Wide Web"

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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.<br>Includes bibliographical references (leaves 85-90). Also available in electronic version. Access restricted to campus users.
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Wang, Hui. "Mining novel Web user behavior models for access prediction /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20WANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 83-91). Also available in electronic version. Access restricted to campus users.
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Nenadić, Oleg. "An implementation of correspondence analysis in R and its application in the analysis of web usage /." Göttingen : Cuvillier, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016229974&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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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.

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The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a ra
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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 User¿s Web Behavior in Websites and Social Blogs." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5269.

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The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a ra
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Lou, Wenwu. "Characterizing Web linking and usage with hierarchical models /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20LOU.

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Linder, Alexander Wehrli Hans Peter. "Web Mining - die Fallstudie Swarovski : theoretische Grundlagen und praktische Anwendungen /." Wiesbaden : Deutscher Universitäts-Verlag, 2005. http://aleph.unisg.ch/hsgscan/hm00135391.pdf.

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Hauck, Roslin V., Homa Atabakhsh, Pichai Ongvasith, Harsh Gupta, and Hsinchun Chen. "Using Coplink to Analyze Criminal-Justice Data." IEEE, 2002. http://hdl.handle.net/10150/105157.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>As information technologies and applications become more overwhelming and diverse, persistent information overload problems have become ever more urgent.1 Fallout from this trend has most affected government, specifically criminaljustice information systems. The explosive growth in the digital information maintained in the data repositories of federal, state, and local criminal-justice entities and the spiraling need for cross-agency access to that information have made utilizing it both increasingly urgent and
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Dehmer, Matthias. "Strukturelle Analyse Web-basierter Dokumente /." Wiesbaden : Dt. Univ-Verl, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014810567&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Kruger, Andries F. "Machine learning, data mining, and the World Wide Web : design of special-purpose search engines." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53492.

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Thesis (MSc)--Stellenbosch University, 2003.<br>ENGLISH ABSTRACT: We present DEADLINER, a special-purpose search engine that indexes conference and workshop announcements, and which extracts a range of academic information from the Web. SVMs provide an efficient and highly accurate mechanism for obtaining relevant web documents. DEADLINER currently extracts speakers, locations (e.g. countries), dates, paper submission (and other) deadlines, topics, program committees, abstracts, and affiliations. Complex and detailed searches are possible on these fields. The niche search engine was const
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Books on the topic "Web usage mining. Data mining. World Wide Web"

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

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From Web to social Web: Discovering and deploying user and content profiles : Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006 : revised selected and invited papers. Springer, 2007.

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David, Hutchison. Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers. Springer Berlin Heidelberg, 2009.

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Mortensen, Dennis R. Yahoo! Web Analytics. John Wiley & Sons, Ltd., 2009.

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Yahoo! Web analytics: Tracking, reporting, and analyzing for data-driven insights. Wiley Technology Pub., 2009.

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Akerkar, Rajendra. Building an intelligent Web: Theory and practice. Jones and Bartlett Publishers, 2008.

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Web content mining with Java: Techniques for exploiting the World Wide Web. John Wiley & Sons, 2002.

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Chang, George. Mining the World Wide Web: An Information Search Approach. Springer US, 2001.

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Carbonell, Jamie G., Jörg Siekmann, Bettina Berendt, et al., eds. Web mining: From web to semantic web : First European Web Mining Forum, EWMF 2003, Cavtat-Dubrovnik, Croatia, September 22, 2003 : invited and selected revised papers. Springer-Verlag, 2004.

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Jain, L. C., and Juan D. Velásquez. Advanced techniques in Web intelligence. Springer, 2010.

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Book chapters on the topic "Web usage mining. Data mining. World Wide Web"

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Abraham, Ajith. "World Wide Web Usage Mining." In Computationally Intelligent Hybrid Systems. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9780471683407.ch11.

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Chang, George, Marcus J. Healey, James A. M. McHugh, and Jason T. L. Wang. "Data Mining." In Mining the World Wide Web. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1639-2_5.

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Sathiyamoorthi V. "Web Usage Mining." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch007.

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In recent days, Internet technology has provided a lot of services for sharing and distributing information across the world. Among all the services, World Wide Web (WWW) plays a significant role. The slow retrieval of Web pages may lessen the interest of users from accessing them. To deal with this problem, Web caching and Web pre-fetching are the two techniques used. Web proxy caching plays a key role in improving Web performance by keeping Web objects that are likely to be used in the near future in the proxy server which is closer to the end user. It helps in reducing user perceived latency, network bandwidth utilization, and alleviating loads on the Web servers. Thus, it improves the efficiency and scalability of Web based system. This chapter gives an overview of Web usage mining and its application on Web and discusses various approaches for improving the performance of Web.
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Kumar, A. V. Senthil, and R. Umagandhi. "Applications of Web Usage Mining across Industries." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch004.

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Web Usage Mining (WUM) is the process of discovery and analysis of useful information from the World Wide Web (WWW) by applying data mining techniques. The main research area in Web mining is focused on learning about Web users and their interactions with Web sites by analysing the log entries from the user log file. The motive of mining is to find users' access models automatically and quickly from the vast Web log data, such as similar queries imposed by the various users, frequent queries applied by the user, frequent web sites visited by the users, clustering of users with similar intent etc. This chapter deals with Web mining, Categories of Web mining, Web usage mining and its process, Applications of Web usage mining across the industries and its related works. This Chapter offers a general knowledge about Web usage mining and its applications for the benefits of researchers those performing research activities in WUM.
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Varaprasad Rao M and Vishnu Murthy G. "DSS for Web Mining Using Recommendation System." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch003.

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Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.
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Sreedhar, G. "Analyzing Website Quality Issues through Web Mining." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch007.

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In the present day scenario the World Wide Web (WWW) is an important and popular information search tool. It provides convenient access to almost all kinds of information – from education to entertainment. The main objective of the chapter is to retrieve information from websites and then use the information for website quality analysis. In this chapter information of the website is retrieved through web mining process. Web mining is the process is the integration of three knowledge domains: Web Content Mining, Web Structure Mining and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of web documents. Web structure mining is the process of inferring knowledge from the World Wide Web organization and links between references and referents in the Web. The web content elements are used to derive functionality and usability of the website. The Web Component elements are used to find the performance of the website. The website structural elements are used to find the complexity and usability of the website. The quality assurance techniques for web applications generally focus on the prevention of web failure or the reduction of chances for such failures. The web failures are defined as the inability to obtain or deliver information such as documents or computational results requested by web users. A high quality website is one that provides relevant, useful content and a good user experience. Thus in this chapter, all areas of website are thoroughly studied for analysing the quality of website design.
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Kumar, Vudattu Kiran. "Knowledge Representation Technologies Using Semantic Web." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch009.

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The World Wide Web (WWW) is global information medium, where users can read and write using computers over internet. Web is one of the services available on internet. The Web was created in 1989 by Sir Tim Berners-Lee. Since then a great refinement has done in the web usage and development of its applications. Semantic Web Technologies enable machines to interpret data published in a machine-interpretable form on the web. Semantic web is not a separate web it is an extension to the current web with additional semantics. Semantic technologies play a crucial role to provide data understandable to machines. To achieve machine understandable, we should add semantics to existing websites. With additional semantics, we can achieve next level web where knowledge repositories are available for better understanding of web data. This facilitates better search, accurate filtering and intelligent retrieval of data. This paper discusses about the Semantic Web and languages involved in describing documents in machine understandable format.
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Sreedhar, G., and A. Anandaraja Chari. "Development of Efficient Decision Support System Using Web Data Mining." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch001.

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The management of web sites imposes a constant demand for new information and timely updates due to the increase of services and content that site owners wish to make available to their users, which in turn is motivated by the complexity and diversity of needs and behaviours of the users. Such constant labour intensive effort implies very high financial and personnel costs. The growth of World Wide Web and technologies has made business functions to be executed fast and easier. E-commerce has provided a cost efficient and effective way of doing business. Web mining is usually defined as the use of data mining techniques to automatically discover and extract information from web documents and services. Also, web data mining is commonly categorized into three areas: web content mining that describes the discovery of useful information from content, web structure mining that analyses the topology of web sites, and web usage mining that tries to make sense of the data generated by the navigation behaviour and user profile.
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Taşci, Tuğrul. "Image Mining." In Intelligent Techniques for Data Analysis in Diverse Settings. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0075-9.ch004.

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In today's World, huge multi-media databases have become evident due to the fact that Internet usage has reached at a very-high level via various types of smart devices. Both willingness to come into prominence commercially and to increase the quality of services in leading areas such as education, health, security and transportation imply querying on those huge multi-media databases. It is clear that description-based querying is almost impossible on such a big unstructured data. Image mining has emerged to that end as a multi-disciplinary field of research which provides example-based querying on image databases. Image mining allows a wide variety of image retrieval and image matching applications intensely required for certain sectors including production, marketing, medicine and web publishing by combining the classical data mining techniques with the implementations of underlying fields such as computer vision, image processing, pattern recognition, machine learning and artificial intelligence.
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Ding, Qin, and Gnanasekaran Sundarraj. "Mining Association Rules from XML Data." In Data Mining and Knowledge Discovery Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch003.

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With the growing usage of XML in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi-structured data, there is an imminent need for tools and techniques to perform data mining on XML documents and XML repositories. In this chapter, we propose a framework for association rule mining on XML data. We present a Java-based implementation of the Apriori and the FP-Growth algorithms for this task and compare their performances. We also compare the performance of our implementation with an XQuery-based implementation.
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Conference papers on the topic "Web usage mining. Data mining. World Wide Web"

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Albanese, 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. ACM Press, 2004. http://dx.doi.org/10.1145/1013367.1013439.

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Papadimitriou, Spiros, and Tina Eliassi-Rad. "Mining Mobility Data." In WWW '15: 24th International World Wide Web Conference. ACM, 2015. http://dx.doi.org/10.1145/2740908.2741987.

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Vidal, R. C. F., N. F. F. Ebecken, and A. G. Evsukoff. "Data mining software using fuzzy inference systems at the World Wide Web." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090151.

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Yuan, Nicholas Jing. "Mining Social and Urban Big Data." In WWW '15: 24th International World Wide Web Conference. ACM, 2015. http://dx.doi.org/10.1145/2740908.2745843.

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Shuai, Hong-Han, Chih-Ya Shen, De-Nian Yang, et al. "Mining Online Social Data for Detecting Social Network Mental Disorders." In WWW '16: 25th International World Wide Web Conference. International World Wide Web Conferences Steering Committee, 2016. http://dx.doi.org/10.1145/2872427.2882996.

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Awekar, Amit C., and Jaewoo Kang. "Selective Approach To Handling Topic Oriented Tasks On The World Wide Web." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368894.

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Atzmueller, Martin, Alvin Chin, and Christoph Trattner. "Session details: Modeling social media: mining big data in social media and the web (MSM 2014)." In WWW '14: 23rd International World Wide Web Conference. ACM, 2014. http://dx.doi.org/10.1145/3254770.

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Abidi, Aman, Rui Zhou, Lu Chen, and Chengfei Liu. "Pivot-based Maximal Biclique Enumeration." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/492.

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Enumerating maximal bicliques in a bipartite graph is an important problem in data mining, with innumerable real-world applications across different domains such as web community, bioinformatics, etc. Although substantial research has been conducted on this problem, surprisingly, we find that pivot-based search space pruning, which is quite effective in clique enumeration, has not been exploited in biclique scenario. Therefore, in this paper, we explore the pivot-based pruning for biclique enumeration. We propose an algorithm for implementing the pivot-based pruning, powered by an effective in
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Whitsitt, Sean, Sonia Vohnout, Timothy Wilmering, Disha Mathad, and Eric Smith. "A Visual Ontological Language for Technical Standards (VOLTS)." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59594.

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Research shows that failures in the standardization process often result from communication and organizational issues between those involved in the committee and the user community. This is mainly caused by two issues: first, a lack of integration of available standards development tools with communication and social interfaces; and second, to the difficulties inherent in organizing and collating information in a semantically meaningful manner. To this effect, the authors present a Visual Ontological Language for Technical Standards (VOLTS). VOLTS is a prototype environment that seeks to addre
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You might also be interested in the extended bibliographies on the topic 'Web usage mining. Data mining. World Wide Web' for particular source types:
Journal articles Dissertations / Theses Books
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