Relevant bibliographies by topics / Web mining

Academic literature on the topic 'Web mining'

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Published: 4 June 2021
Last updated: 5 March 2023

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Journal articles on the topic "Web mining"

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Stoffel, Kilian. "Web + Data Mining = Web Mining." HMD Praxis der Wirtschaftsinformatik 46, no. 4 (August 2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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Walther, Ralf. "Web Mining." Informatik-Spektrum 24, no. 1 (February 20, 2001): 16–18. http://dx.doi.org/10.1007/s002870100145.

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Agyapong, Kwame, J. B. Hayfron Acquah, and M. Asante. "AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING." International Journal of Engineering Technologies and Management Research 4, no. 8 (February 1, 2020): 22–27. http://dx.doi.org/10.29121/ijetmr.v4.i8.2017.91.

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With the rapid increase in internet technology, users get easily confused in large hypertext structure. The primary goal of the web site owner is to provide the relevant information to the users to fulfill their needs. In order to achieve this goal, they use the concept of web mining. Web mining is used to categorize users and pages by analyzing the users‟ behaviour, the content of the pages, and the order of the URLs that tend to be accessed in order. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. It is very difficult for a user to find the high quality information which he wants. Page Ranking algorithm is needed which provide the higher ranking to the important pages. In this paper, we discuss the improvement of Page ranking algorithm to provide the higher ranking to important pages. Most of the search engines are ranking their search results in response to user’s queries to make their search navigations easier.
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Vartak, Amey. "Web Personalization using Web Mining." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 86–89. http://dx.doi.org/10.22214/ijraset.2018.4019.

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Eirinaki, Magdalini, and Michalis Vazirgiannis. "Web mining for web personalization." ACM Transactions on Internet Technology 3, no. 1 (February 2003): 1–27. http://dx.doi.org/10.1145/643477.643478.

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Skaria, Bibu, Dr Eldhose T John, and P. X. Shajan. "Literature Review on Web Mining." Bonfring International Journal of Data Mining 6, no. 1 (January 31, 2016): 04–06. http://dx.doi.org/10.9756/bijdm.8127.

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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 (July 2012): 1–12. http://dx.doi.org/10.4018/jtd.2012070101.

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The technological revolutions have opened up new ways of information and communication. The Internet is growing as a vital source of information in this modern era of technology. The ever increasing volume of information through WWW is creating complexity in the design, development and deployment of WWW. It has become important for the organizations to analyze the usage of their web sites. The web usage analysis may help the organizations not only to monitor the load on their websites and cater for the needs of their potential clients but also enhance their web services and restructure the organization to better serve their clients. Web mining has emerged as important research areas used to discover information which can be utilized for improvement of websites. Allama Iqbal Open University (AIOU) is one of the largest open and distant university of the world. Due to unique philosophy of open and distant learning, AIOU has been providing useful information online through its website. It is an active website which is flooded with huge flow of information. This paper presents web usage analysis of AIOU website and provides statistical analysis of the usage patterns. It presents how the results were used not only to enhance the web contents and services but also discusses how these results helped the university to allocate and reallocate its resources. The reallocation was used to improve efficiency and processes of the university in order to better serve its clients.
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Nasraoui, Olfa. "Web data mining." ACM SIGKDD Explorations Newsletter 10, no. 2 (December 20, 2008): 23–25. http://dx.doi.org/10.1145/1540276.1540281.

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Stumme, Gerd, Andreas Hotho, and Bettina Berendt. "Semantic Web Mining." Journal of Web Semantics 4, no. 2 (June 2006): 124–43. http://dx.doi.org/10.1016/j.websem.2006.02.001.

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Dissertations / Theses on the topic "Web mining"

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Zheng, George. "Web Service Mining." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26324.

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In this dissertation, we present a novel approach for Web service mining. Web service mining is a new research discipline. It is different from conventional top down service composition approaches that are driven by specific search criteria. Web service mining starts with no such criteria and aims at the discovery of interesting and useful compositions of existing Web services. Web service mining requires the study of three main research topics: semantic description of Web services, efficient bottom up composition of composable services, and interestingness and usefulness evaluation of composed services. We first propose a Web service ontology to describe and organize the constructs of a Web service. We introduce the concept of Web service operation interface for the description of shared Web service capabilities and use Web service domains for grouping Web service capabilities based on these interfaces. We take clues from how Nature solves the problem of molecular composition and introduce the notion of Web service recognition to help devise efficient bottom up service composition strategies. We introduce several service recognition mechanisms that take advantage of the domain-based categorization of Web service capabilities and ontology-based description of operation semantics. We take clues from the drug discovery process and propose a Web service mining framework to group relevant mining activities into a progression of phases that would lead to the eventual discovery of useful compositions. Based on the composition strategies that are derived from recognition mechanisms, we propose a set of algorithms in the screening phase of the framework to automatically identify leads of service compositions. We propose objective interestingness and usefulness measures in the evaluation phase to narrow down the pool of composition leads for further exploration. To demonstrate the effectiveness of our framework and to address challenges faced by existing biological data representation methodologies, we have applied relevant techniques presented in this dissertation to the field of biological pathway discovery.
Ph. D.
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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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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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Martins, Bruno. "Geographically Aware Web Text Mining." Master's thesis, Department of Informatics, University of Lisbon, 2009. http://hdl.handle.net/10451/14301.

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Text mining and search have become important research areas over the past few years, mostly due to the large popularity of the Web. A natural extension for these technologies is the development of methods for exploring the geographic context of Web information. Human information needs often present specific geographic constraints. Many Web documents also refer to speci c locations. However, relatively little e ort has been spent on developing the facilities required for geographic access to unstructured textual information. Geographically aware text mining and search remain relatively unexplored. This thesis addresses this new area, arguing that Web text mining can be applied to extract geographic context information, and that this information can be explored for information retrieval. Fundamental questions investigated include handling geographic references in text, assigning geographic scopes to the documents, and building retrieval applications that handle/use geographic scopes. The thesis presents appropriate solutions for each of these challenges, together with a comprehensive evaluation of their efectiveness. By investigating these questions, the thesis presents several findings on how the geographic context can be efectively handled by text processing tools.
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Stavrianou, Anna. "Modeling and mining of Web discussions." Phd thesis, Université Lumière - Lyon II, 2010. http://tel.archives-ouvertes.fr/tel-00564764.

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Le développement du Web 2.0 a donné lieu à la production d'une grande quantité de discussions en ligne. La fouille et l'extraction de données de qualité de ces discussions en ligne sont importantes dans de nombreux domaines (industrie, marketing) et particulièrement pour toutes les applications de commerce électronique. Les discussions de ce type contiennent des opinions et des croyances de personnes et cela explique l'intérêt de développer des outils d'analyse efficaces pour ces discussions. L'objectif de cette thèse est de définir un modèle qui représente les discussions en ligne et facilite leur analyse. Nous proposons un modèle basé sur des graphes. Les sommets du graphe représentent les objets de type message. Chaque objet de type message contient des informations comme son contenu, son auteur, l'orientation de l'opinion qui y été exprimée et la date où il a été posté. Les liens parmi les objets message montrent une relation de type "répondre à". En d'autres termes, ils montrent quels objets répondent à quoi, conséquence directe de la structure de la discussion en ligne. Avec ce nouveau modèle, nous proposons un certain nombre de mesures qui guident la fouille au sein de la discussion et permettent d'extraire des informations pertinentes. Il existe des mesures centrées sur l'analyse de l'opinion qui traitent de l'évolution de l'opinion au sein de la discussion. Nous définissons également des mesures centrées sur le temps, qui exploitent la dimension temporelle du modèle, alors que les mesures centrées sur le sujet peuvent être utilisées pour mesurer la présence de sujets dans une discussion. La présence de l'utilisateur dans des discussions en ligne peut être exploitée soit par les techniques des réseaux sociaux, soit à travers notre nouveau modèle qui inclut la connaissance des auteurs de chaque objet message. De plus, une liste de messages clés est recommandée à l'utilisateur pour permettre une participation plus efficace au sein de la discussion.
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Norguet, 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.

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With the emergence of the Internet and of the World Wide Web, the Web site has become a key communication channel in organizations. To satisfy the objectives of the Web site and of its target audience, adapting the Web site content to the users' expectations has become a major concern. In this context, Web usage mining, a relatively new research area, and Web analytics, a part of Web usage mining that has most emerged in the corporate world, offer many Web communication analysis techniques. These techniques include prediction of the user's behaviour within the site, comparison between expected and actual Web site usage, adjustment of the Web site with respect to the users' interests, and mining and analyzing Web usage data to discover interesting metrics and usage patterns. However, Web usage mining and Web analytics suffer from significant drawbacks when it comes to support the decision-making process at the higher levels in the organization.

Indeed, 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

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Chen, Hsinchun. "Special issue: "Web retrieval and mining"." Elsevier, 2003. http://hdl.handle.net/10150/106101.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Search engines and data mining are two research areas that have experienced significant progress over the past few years. Overwhelming acceptance of the Internet as a primary medium for content delivery and business transactions has created unique opportunities and challenges for researchers. The richness of the webâ s multimedia content, the reach and timeliness of web-based publication, the proliferation of e-commerce activities and the potential for wireless web delivery have generated many interesting research problems. Technical, system, organizational and social research approaches are all needed to address these research problems. Many interesting webretrieval and mining research topics have emerged recently. These include, but are not limited to, the following: text and data mining on the web, web visualization, web intelligence and agents, web-based decision support and knowledge management, wireless web retrieval and visualization, web-based usability methodology, web-based analysis for eCommerce applications. This special issue consists of nine papers that report research in web retrieval and mining.
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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/.

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[Abstract]: Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalising the Web users’ browsing experiences, assists Web masters in the improvement of the Web site structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page tobe accessed by theWeb user based on the user’s previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especiallywhen it comes to accuracy and space complexity. This dissertation achieves better accuracy as well as less state space complexity and rules generated by performingthe following combinations. First, we combine low-order Markov model and association rules. Markov model analysis are performed on the data sets. If the Markov model prediction results in a tie or no state, association rules are used for prediction. The outcome of this integration is better accuracy, less Markov model state space complexity and less number of generated rules than using each of the methods individually. Second, we integrate low-order Markov model and clustering. The data sets are clustered and Markov model analysis are performed oneach cluster instead of the whole data sets. The outcome of the integration is better accuracy than the first combination with less state space complexity than higherorder Markov model. The last integration model involves combining all three techniques together: clustering, association rules and low-order Markov model. The data sets are clustered and Markov model analysis are performed on each cluster. If the Markov model prediction results in close accuracies for the same item, association rules are used for prediction. This integration model achievesbetter Web page access prediction accuracy, less Markov model state space complexity and less number of rules generated than the previous two models.
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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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Nagi, 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.

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The main motivation for the study described in this dissertation is to benefit from the development in technology and the huge amount of available data which can be easily captured, stored and maintained electronically. We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We developed a framework that integrates statistical analysis, frequent pattern mining, clustering, classification and network construction and analysis. We concentrated on the statistical data related to the visitors and how they surf and pass through the various pages of a given web site to land at some target pages. Further, the frequent pattern mining technique was used to study the relationship between the various pages constituting a given web site. Clustering is used to study the similarity of users and pages. Classification suggests a target class for a given new entity by comparing the characteristics of the new entity to those of the known classes. Network construction and analysis is also employed to identify and investigate the links between the various pages constituting a Web site by constructing a network based on the frequency of access to the Web pages such that pages get linked in the network if they are identified in the result of the frequent pattern mining process as frequently accessed together. The knowledge discovered by analysing a web site and its related data should be considered valuable for online shoppers and commercial web site owners. Benefitting from the outcome of the study, a recommendation system was developed to suggest pages to visitors based on their profiles as compared to similar profiles of other visitors. The conducted experiments using popular datasets demonstrate the applicability and effectiveness of the proposed framework for Web mining and recommendation. As a by product of the proposed method, we demonstrate how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5.
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Books on the topic "Web mining"

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Liu, Bing. Web Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3.

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Zheng, George, and Athman Bouguettaya. Web Service Mining. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6539-4.

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Berendt, Bettina, Andreas Hotho, Dunja Mladenič, Maarten van Someren, Myra Spiliopoulou, and Gerd Stumme, eds. Web Mining: From Web to Semantic Web. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b100615.

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Omitola, Tope, Sebastián A. Ríos, and John G. Breslin. Social Semantic Web Mining. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-79459-9.

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Mukhopadhyay, Debajyoti, ed. Web Searching and Mining. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3053-7.

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Ackermann, Markus, Bettina Berendt, Marko Grobelnik, Andreas Hotho, Dunja Mladenič, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtěch Svátek, and Maarten van Someren, eds. Semantics, Web and Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908678.

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Mining the social web. Sebastopol, CA: O'Reilly, 2011.

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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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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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Book chapters on the topic "Web mining"

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Chang, George, Marcus J. Healey, James A. M. McHugh, and Jason T. L. Wang. "Web Mining." In Mining the World Wide Web, 93–104. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1639-2_7.

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Linder, Alexander. "Web Mining." In Web Mining — Die Fallstudie Swarovski, 63–87. Wiesbaden: Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81252-0_3.

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Fürnkranz, Johannes. "Web Mining." In Data Mining and Knowledge Discovery Handbook, 913–29. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_47.

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Sarukkai, Ramesh R. "Web Mining." In Foundations of Web Technology, 139–75. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1135-9_6.

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Kumbhar, V. S., K. S. Oza, and R. K. Kamat. "Current Literature Assessment in Data and Web Mining." In Web Mining, 37–53. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003340034-2.

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Kumbhar, V. S., K. S. Oza, and R. K. Kamat. "Introduction." In Web Mining, 1–36. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003340034-1.

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Kumbhar, V. S., K. S. Oza, and R. K. Kamat. "Classification of Websites." In Web Mining, 89–197. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003340034-4.

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Kumbhar, V. S., K. S. Oza, and R. K. Kamat. "DataSet Creation for Web Mining." In Web Mining, 55–88. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003340034-3.

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Ghani, Rayid. "Mining the Web to Add Semantics to Retail Data Mining." In Web Mining: From Web to Semantic Web, 43–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30123-3_3.

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Aschenbrenner, Andreas, and Andreas Rauber. "Mining Web Collections." In Web Archiving, 153–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-46332-0_7.

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Conference papers on the topic "Web mining"

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Kumar, Ravi. "Mining web logs." In the 15th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557022.

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Sudhamathy, G. "Mining web logs." In the 1st Amrita ACM-W Celebration. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858435.

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Wibonele, Kasanda J., and Yanqing Zhang. "Web data mining." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460233.

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Harb, Ali, Michel Plantié, Gerard Dray, Mathieu Roche, François Trousset, and Pascal Poncelet. "Web opinion mining." In the 5th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456269.

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Ester, Martin, Hans-Peter Kriegel, and Matthias Schubert. "Web site mining." In the eighth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/775047.775084.

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Youssefi, Amir H., David J. Duke, and Mohammed J. Zaki. "Visual web mining." In the 13th international World Wide Web conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1013367.1013492.

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Sun, Aixin, and Ee-Peng Lim. "Web unit mining." In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956863.956885.

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Rajput, Anil, and Nidhi Chandel. "Web usage mining." In the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980384.

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Griazev, Kiril, and Simona Ramanauskaite. "Web mining taxonomy." In 2018 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE, 2018. http://dx.doi.org/10.1109/estream.2018.8394124.

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Bharti, Pooja M., and Tushar J. Raval. "Improving Web Page Access Prediction using Web Usage Mining and Web Content Mining." In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2019. http://dx.doi.org/10.1109/iceca.2019.8821950.

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Reports on the topic "Web mining"

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Joshi, Anupam, and Raghu Krishnapuram. On Mining Web Access Logs. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada461525.

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Resnik, P. Parallel Strands: A Preliminary Investigation into Mining the Web. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada458649.

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Hoyt, Robert, Hui-Min Chung, Brent Hutfless, Justice Mbizo, and Courtney Rice. Creating a Web-Based Family History Questionnaire for Data Mining. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada578129.

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Lin, Jimmy, Aaron Fernandes, Boris Katz, Gregory Marton, and Stefanie Tellex. Extracting Answers from the Web Using Knowledge Annotation and Knowledge Mining Techniques. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456267.

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Knox, Sally, Kïrsten Way, and Alex Haslam. Are identity leadership and shared social identity associated with the highly reliable behaviour of military personnel? Protocol for a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0063.

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Review question / Objective: Are identity leadership and shared social identity associated with the highly reliable behaviour of military personnel? Information sources: Searches will be conducted in the following databases: PsychInfo, Web of Sciences, Proquest Social Science Database, PTSDpubs, PubMed, Business Source Complete, and SCOPUS. To ensure literature saturation, the eligible papers and reviews identified through the search will be used for reference mining. A bibliography of the eligible papers will be circulated to the systematic review team and social identity experts identified by the team to ensure all relevant material has been captured.
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Nenci, Silvia, and Francesco Quatraro. Innovation and Competitiveness in Mining Value Chains in Latin America. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003805.

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This paper provides an international overview of the mining global value chain (GVC) and its most recent transformations and trends, focusing on Argentina, Brazil, and Peru. The study uses international trade data and patent and scientific publications data. By using trade in value added, we first investigate the role of those countries in the international mining trade, and their specialization, participation, and position in the mining GVC for the period 2005-15. The analysis is carried out for both mining products and mining-related services, and also looks at the contribution of services to mining exports. Second, we analyze the evolution of innovative activity and the direction of technological change in the mining sector over the past 40 years by looking at patent applications, both internationally and with attention to the three target countries. We also provide an overview of, and some insights on, knowledge flow in the mining sector based on scientific production.
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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.
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Kwasnitschka, Tom. Deep-Ocean Validation of the LIGHTHOUSE System - Cruise No. AL568, 11.11.21 – 22.11.21, Kiel (Germany) – Kiel (Germany) - LIGHTHOUSE-TEST III. Alkor-Berichte AL568. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al568.

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The objective of this cruise was to conduct the final and complete field test of the LIGHTHOUSE situational awareness system for remotely operated vehicles, developed in the HVF 0068 Project LIGHTHOUSE. This included three divesof the ROV PHOCAin the Norwegian Sognefjord, during which the optical and acoustic sensors were validated. Moreover, as part of the EU H2020 project iAtlantic (grant agreement 818123), we investigated the response of pelagic deep-sea fauna to warmingand suspended sediment (which will be introduced to pelagic ecosystems by deep-sea mining activities). To this end, we captured the jellyfish Periphylla periphyllaand conducted shipboard experiments.
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