Relevant bibliographies by topics / Web document clustering (WDC)

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  2. Dissertations / Theses
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Academic literature on the topic 'Web document clustering (WDC)'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Web document clustering (WDC)"

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Rani Manukonda, Sumathi, Asst Prof Kmit, Narayanguda ., et al. "Efficient Document Clustering for Web Search Result." International Journal of Engineering & Technology 7, no. 3.3 (2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.3.14494.

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Clustering the document in data mining is one of the traditional approach in which the same documents that are more relevant are grouped together. Document clustering take part in achieving accuracy that retrieve information for systems that identifies the nearest neighbors of the document. Day to day the massive quantity of data is being generated and it is clustered. According to particular sequence to improve the cluster qualityeven though different clustering methods have been introduced, still many challenges exist for the improvement of document clustering. For web search purposea docume
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Im, Yeong-Hui. "A Post Web Document Clustering Algorithm." KIPS Transactions:PartB 9B, no. 1 (2002): 7–16. http://dx.doi.org/10.3745/kipstb.2002.9b.1.007.

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He, Xiaofeng, Hongyuan Zha, Chris H.Q. Ding, and Horst D. Simon. "Web document clustering using hyperlink structures." Computational Statistics & Data Analysis 41, no. 1 (2002): 19–45. http://dx.doi.org/10.1016/s0167-9473(02)00070-1.

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Creţulescu, Radu G., Daniel I. Morariu, Macarie Breazu, and Daniel Volovici. "DBSCAN Algorithm for Document Clustering." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 9, no. 1 (2019): 58–66. http://dx.doi.org/10.2478/ijasitels-2019-0007.

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AbstractDocument clustering is a problem of automatically grouping similar document into categories based on some similarity metrics. Almost all available data, usually on the web, are unclassified so we need powerful clustering algorithms that work with these types of data. All common search engines return a list of pages relevant to the user query. This list needs to be generated fast and as correct as possible. For this type of problems, because the web pages are unclassified, we need powerful clustering algorithms. In this paper we present a clustering algorithm called DBSCAN – Density-Bas
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Hammouda, K. M., and M. S. Kamel. "Efficient phrase-based document indexing for Web document clustering." IEEE Transactions on Knowledge and Data Engineering 16, no. 10 (2004): 1279–96. http://dx.doi.org/10.1109/tkde.2004.58.

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Chawla, Suruchi. "Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijirr.300367.

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The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data
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Shen Huang, Zheng Chen, Yong Yu, and Wei-Ying Ma. "Multitype features coselection for Web document clustering." IEEE Transactions on Knowledge and Data Engineering 18, no. 4 (2006): 448–59. http://dx.doi.org/10.1109/tkde.2006.1599384.

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Chan, Samuel W. K., and Mickey W. C. Chong. "Unsupervised clustering for nontextual web document classification." Decision Support Systems 37, no. 3 (2004): 377–96. http://dx.doi.org/10.1016/s0167-9236(03)00035-6.

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Boley, Daniel, Maria Gini, Robert Gross, et al. "Partitioning-based clustering for Web document categorization." Decision Support Systems 27, no. 3 (1999): 329–41. http://dx.doi.org/10.1016/s0167-9236(99)00055-x.

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Su, Zhong, Qiang Yang, Hongjiang Zhang, Xiaowei Xu, Yu-Hen Hu, and Shaoping Ma. "Correlation-Based Web Document Clustering for Adaptive Web Interface Design." Knowledge and Information Systems 4, no. 2 (2002): 151–67. http://dx.doi.org/10.1007/s101150200002.

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Dissertations / Theses on the topic "Web document clustering (WDC)"

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Coquet, Jean. "Étude exhaustive de voies de signalisation de grande taille par clustering des trajectoires et caractérisation par analyse sémantique." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S073/document.

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Les voies de signalisation décrivent les réponses d'une cellule à des stimuli externes. Elles sont primordiales dans les processus biologiques tels que la différentiation, la prolifération ou encore l'apoptose. La biologie des systèmes tentent d'étudier ces voies de façon exhaustive à partir de modèles statistiques ou dynamiques. Le nombre de solutions expliquant un phénomène biologique (par exemple la réaction d'une cellule à un stimulus) peut être très élevé dans le cas de grands modèles. Cette thèse propose, dans un premier temps, différentes stratégies de regroupement de ces solutions à pa
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Roussinov, Dmitri G., and Hsinchun Chen. "Document clustering for electronic meetings: an experimental comparison of two techniques." Elsevier, 1999. http://hdl.handle.net/10150/105091.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>In this article, we report our implementation and comparison of two text clustering techniques. One is based on Wardâ s clustering and the other on Kohonenâ s Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to â â clean upâ â the automatically produced clusters. The technique based on Wardâ s clustering was found to be more precise. Both techniques have worked equally well in dete
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Kellou-Menouer, Kenza. "Découverte de schéma pour les données du Web sémantique." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV047/document.

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Un nombre croissant de sources de données interconnectées sont publiées sur le Web. Cependant, leur schéma peut êtreincomplet ou absent. De plus, les données ne sont pas nécessairement conformes au schéma déclaré. Ce qui rend leur exploitation complexe. Dans cette thèse, nous proposons une approche d’extraction automatique et incrémentale du schéma d’une source à partir de la structure implicite de ses données. Afin decompléter la description des types découverts, nous proposons également une approche de découverte des patterns structurels d’un type. L’approche procède en ligne sans avoir à té
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Zanghi, Hugo. "Approches modèles pour la structuration du web vu comme un graphe." Thesis, Evry-Val d'Essonne, 2010. http://www.theses.fr/2010EVRY0041/document.

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L’analyse statistique des réseaux complexes est une tâche difficile, étant donné que des modèles statistiques appropriés et des procédures de calcul efficaces sont nécessaires afin d’apprendre les structures sous-jacentes. Le principe de ces modèles est de supposer que la distribution des valeurs des arêtes suit une distribution paramétrique, conditionnellement à une structure latente qui est utilisée pour détecter les formes de connectivité. Cependant, ces méthodes souffrent de procédures d’estimation relativement lentes, puisque les dépendances sont complexes. Dans cette thèse nous adaptons
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Qumsiyeh, Rani Majed. "Easy to Find: Creating Query-Based Multi-Document Summaries to Enhance Web Search." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2713.

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Current web search engines, such as Google, Yahoo!, and Bing, rank the set of documents S retrieved in response to a user query Q and display each document with a title and a snippet, which serves as an abstract of the corresponding document in S. Snippets, however, are not as useful as they are designed for, i.e., to assist search engine users to quickly identify results of interest, if they exist, without browsing through the documents in S, since they (i) often include very similar information and (ii) do not capture the main content of the corresponding documents. Moreover, when the intend
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Saoud, Zohra. "Approche robuste pour l’évaluation de la confiance des ressources sur le Web." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1331/document.

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Cette thèse en Informatique s'inscrit dans le cadre de gestion de la confiance et plus précisément des systèmes de recommandation. Ces systèmes sont généralement basés sur les retours d'expériences des utilisateurs (i.e., qualitatifs/quantitatifs) lors de l'utilisation des ressources sur le Web (ex. films, vidéos et service Web). Les systèmes de recommandation doivent faire face à trois types d'incertitude liés aux évaluations des utilisateurs, à leur identité et à la variation des performances des ressources au fil du temps. Nous proposons une approche robuste pour évaluer la confiance en ten
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Ghenname, Mérième. "Le web social et le web sémantique pour la recommandation de ressources pédagogiques." Thesis, Saint-Etienne, 2015. http://www.theses.fr/2015STET4015/document.

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Ce travail de recherche est conjointement effectué dans le cadre d’une cotutelle entre deux universités : en France l’Université Jean Monnet de Saint-Etienne, laboratoire Hubert Curien sous la supervision de Mme Frédérique Laforest, M. Christophe Gravier et M. Julien Subercaze, et au Maroc l’Université Mohamed V de Rabat, équipe LeRMA sous la supervision de Mme Rachida Ajhoun et Mme Mounia Abik. Les connaissances et les apprentissages sont des préoccupations majeures dans la société d’aujourd’hui. Les technologies de l’apprentissage humain visent à promouvoir, stimuler, soutenir et valider le
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Luu, Vinh Trung. "Using event sequence alignment to automatically segment web users for prediction and recommendation." Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH0098/document.

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Une masse de données importante est collectée chaque jour par les gestionnaires de site internet sur les visiteurs qui accèdent à leurs services. La collecte de ces données a pour objectif de mieux comprendre les usages et d'acquérir des connaissances sur le comportement des visiteurs. A partir de ces connaissances, les gestionnaires de site peuvent décider de modifier leur site ou proposer aux visiteurs du contenu personnalisé. Cependant, le volume de données collectés ainsi que la complexité de représentation des interactions entre le visiteur et le site internet nécessitent le développement
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Anderson, James D. "Interactive Visualization of Search Results of Large Document Sets." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547048073451373.

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Attiaoui, Dorra. "Belief detection and temporal analysis of experts in question answering communities : case strudy on stack overflow." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S085/document.

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L'émergence du Web 2.0 a changé la façon avec laquelle les gens recherchent et obtiennent des informations sur internet. Entre sites communautaires spécialisés, réseaux sociaux, l'utilisateur doit faire face à une grande quantité d'informations. Les sites communautaires de questions réponses représentent un moyen facile et rapide pour obtenir des réponses à n'importe quelle question qu'une personne se pose. Tout ce qu'il suffit de faire c'est de déposer une question sur un de ces sites et d'attendre qu'un autre utilisateur lui réponde. Dans ces sites communautaires, nous voulons
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Books on the topic "Web document clustering (WDC)"

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Introduction to Information Retrieval. Cambridge University Press, 2008.

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Manning, Christopher D., Hinrich Schütze, and Prabhakar Raghavan. Introduction to Information Retrieval. Cambridge University Press, 2008.

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Manning, Christopher D. Introduction to Information Retrieval. Cambridge University Press, 2008.

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Manning, Christopher D., Hinrich Schütze, and Prabhakar Raghavan. Introduction to Information Retrieval. Cambridge University Press, 2012.

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Book chapters on the topic "Web document clustering (WDC)"

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Schenker, Adam, Mark Last, Horst Bunke, and Abraham Kandel. "Graph Representations for Web Document Clustering." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_108.

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Qian, Tieyun, Jianfeng Si, Qing Li, and Qian Yu. "Leveraging Network Structure for Incremental Document Clustering." In Web Technologies and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29253-8_29.

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Huang, Shen, Gui-Rong Xue, Ben-Yu Zhang, Zheng Chen, Yong Yu, and Wei-Ying Ma. "Multi-type Features Based Web Document Clustering." In Web Information Systems – WISE 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30480-7_27.

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Wong, Wai-chiu, and Ada Wai-chee Fu. "Incremental Document Clustering for Web Page Classification." In Enabling Society with Information Technology. Springer Japan, 2002. http://dx.doi.org/10.1007/978-4-431-66979-1_10.

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Oikonomakou, N., and M. Vazirgiannis. "A Review of Web Document Clustering Approaches." In Text Mining and its Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45219-5_6.

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Oikonomakou, Nora, and Michalis Vazirgiannis. "A Review of Web Document Clustering Approaches." In Data Mining and Knowledge Discovery Handbook. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_48.

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Wei, Yang, Jinmao Wei, and Zhenglu Yang. "Extended Strategies for Document Clustering with Word Co-occurrences." In Web Technologies and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25255-1_38.

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Singh, Amit Prakash, Shalini Srivastava, and Sanjib Kumar Sahu. "Phrase Based Web Document Clustering: An Indexing Approach." In Lecture Notes in Networks and Systems. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3226-4_49.

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Li, Peng, Bin Wang, Wei Jin, and Yachao Cui. "User-Related Tag Expansion for Web Document Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20161-5_5.

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Zaw, Moe Moe, and Ei Ei Mon. "Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm." In Studies in Computational Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13826-8_14.

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Conference papers on the topic "Web document clustering (WDC)"

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Han, Juhyun, Taehwan Kim, and Joongmin Choi. "Web Document Clustering by Using Automatic Keyphrase Extraction." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wi-iatw.2007.46.

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Han, Juhyun, Taehwan Kim, and Joongmin Choi. "Web Document Clustering by Using Automatic Keyphrase Extraction." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wiiatw.2007.4427539.

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Yang, Yu-Jiu, and Bao-Gang Hu. "Pairwise Constraints-Guided Non-negative Matrix Factorization for Document Clustering." In IEEE/WIC/ACM International Conference on Web Intelligence (WI'07). IEEE, 2007. http://dx.doi.org/10.1109/wi.2007.66.

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Zhou, X. F., J. G. Liang, Y. Hu, and L. Guo. "Text Document Latent Subspace Clustering by PLSA Factors." In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2014. http://dx.doi.org/10.1109/wi-iat.2014.131.

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Aliguliyev, Ramiz. "A Novel Partitioning-Based Clustering Method and Generic Document Summarization." In 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops. IEEE, 2006. http://dx.doi.org/10.1109/wi-iatw.2006.16.

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Zhao, Weizhong, Qing He, Huifang Ma, and Zhongzhi Shi. "Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.45.

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Zamir, Oren, and Oren Etzioni. "Web document clustering." In the 21st annual international ACM SIGIR conference. ACM Press, 1998. http://dx.doi.org/10.1145/290941.290956.

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Momin, B. F., P. J. Kulkarni, and Amol Chaudhari. "Web Document Clustering Using Document Index Graph." In 2006 International Conference on Advanced Computing and Communications. IEEE, 2006. http://dx.doi.org/10.1109/adcom.2006.4289851.

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Tekir, Selma, Florian Mansmann, and Daniel Keim. "Geodesic distances for web document clustering." In 2011 Ieee Symposium On Computational Intelligence And Data Mining - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cidm.2011.5949449.

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Liu, Debao, Dan Yang, Tiezheng Nie, Yue Kou, and Derong Shen. "Document Clustering in Personal Dataspace." In 2010 7th Web Information Systems and Applications Conference (WISA). IEEE, 2010. http://dx.doi.org/10.1109/wisa.2010.16.

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Reports on the topic "Web document clustering (WDC)"

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He, Xiaofeng, Hongyuan Zha, Chris H. Q. Ding, and Horst D. Simon. Web document clustering using hyperlink structures. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/815474.

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