Relevant bibliographies by topics / Clustering of text information

Contents

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

Academic literature on the topic 'Clustering of text information'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Clustering of text information"

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Vedmiediev, Daniil, and Nataliia Shapoval. "Text Message Clustering." Electronics and Control Systems 4, no. 78 (2023): 16–20. http://dx.doi.org/10.18372/1990-5548.78.18255.

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The division into groups of text messages is considered, which can be useful when building a personalized approach in different systems. Тo solve this problem, the Embedded Word2Vec was proposed. To enhance the division into groups, the suggestion of employing mini-batch k-means is presented, offering a method with lower computational demands. This recommendation aligns with the practical need for efficient and scalable clustering methods, especially when dealing with large datasets. Furthermore, the proposed metric based on the greatest common sequence is highlighted as a valuable tool for ev
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Strouse, DJ, and David J. Schwab. "The Information Bottleneck and Geometric Clustering." Neural Computation 31, no. 3 (2019): 596–612. http://dx.doi.org/10.1162/neco_a_01136.

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The information bottleneck (IB) approach to clustering takes a joint distribution [Formula: see text] and maps the data [Formula: see text] to cluster labels [Formula: see text], which retain maximal information about [Formula: see text] (Tishby, Pereira, & Bialek, 1999 ). This objective results in an algorithm that clusters data points based on the similarity of their conditional distributions [Formula: see text]. This is in contrast to classic geometric clustering algorithms such as [Formula: see text]-means and gaussian mixture models (GMMs), which take a set of observed data points [Fo
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Zhang, Wen, Taketoshi Yoshida, Xijin Tang, and Qing Wang. "Text clustering using frequent itemsets." Knowledge-Based Systems 23, no. 5 (2010): 379–88. http://dx.doi.org/10.1016/j.knosys.2010.01.011.

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Nasir, Jamal A., Iraklis Varlamis, Asim Karim, and George Tsatsaronis. "Semantic smoothing for text clustering." Knowledge-Based Systems 54 (December 2013): 216–29. http://dx.doi.org/10.1016/j.knosys.2013.09.012.

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Fan, Wentao. "Application and analysis of text similarity in text clustering in the Chinese context." Applied and Computational Engineering 21, no. 1 (2023): 71–77. http://dx.doi.org/10.54254/2755-2721/21/20231120.

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With the development of the Internet, information sharing is higher, and the amount of information that each user is exposed to is increasing. How to find the information peoples want from so much information is a very important question. The vast majority of these resources are related to textual information. The most intuitive manifestation of these problems is that when people usually use search engines, enter a piece of text, and search out the relevant website, if the algorithm is not good, the search results will be very unsatisfactory. Therefore, this paper studies the application of te
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Papapetrou, Odysseas, Wolf Siberski, and Norbert Fuhr. "Decentralized Probabilistic Text Clustering." IEEE Transactions on Knowledge and Data Engineering 24, no. 10 (2012): 1848–61. http://dx.doi.org/10.1109/tkde.2011.120.

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Bewoor, Mrunal S., and Suhas H. Patil. "Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms." Engineering, Technology & Applied Science Research 8, no. 1 (2018): 2562–67. https://doi.org/10.5281/zenodo.1207394.

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<em>Abstract</em>&mdash;The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algo
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Kogan, J., C. Nicholas, and V. Volkovich. "Data Mining - Text mining with information-theoretic clustering." Computing in Science & Engineering 5, no. 6 (2003): 52–59. http://dx.doi.org/10.1109/mcise.2003.1238704.

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Liu, Yuan-chao, Chong Wu, and Ming Liu. "Research of fast SOM clustering for text information." Expert Systems with Applications 38, no. 8 (2011): 9325–33. http://dx.doi.org/10.1016/j.eswa.2011.01.126.

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Onan, Aytug, Hasan Bulut, and Serdar Korukoglu. "An improved ant algorithm with LDA-based representation for text document clustering." Journal of Information Science 43, no. 2 (2016): 275–92. http://dx.doi.org/10.1177/0165551516638784.

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Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are fr
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Dissertations / Theses on the topic "Clustering of text information"

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Rosell, Magnus. "Text Clustering Exploration : Swedish Text Representation and Clustering Results Unraveled." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10129.

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Text clustering divides a set of texts into clusters (parts), so that texts within each cluster are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on familiar ones. The main contributions of this thesis are an investigation of text representation for Swedish and some extensions of the work on how to use text clustering as an exploration tool. We have also done some work on synonyms and evaluation of clustering results. Text clustering, at least such as it is treated here, is performed using the vector space model
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Viana, Hugo Henrique Amorim. "Automatic information retrieval through text-mining." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11308.

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The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia<br>Nowadays, around a huge amount of firms in the European Union catalogued as Small and Medium Enterprises (SMEs), employ almost a great portion of the active workforce in Europe. Nonetheless, SMEs cannot afford implementing neither methods nor tools to systematically adapt innovation as a part of their business process. Innovation is the engine to be competitive in the globalized environment, especially in the current
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Wang, Wei. "Unsupervised Information Extraction From Text - Extraction and Clustering of Relations between Entities." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00998390.

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Unsupervised information extraction in open domain gains more and more importance recently by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, this thesis focuses on the tasks of extraction and clustering of relations between entities at a large scale. The objective of relation extraction is to discover unknown relations from texts. A relation prototype is first defined, with which candidates of relation instances are initially extracted wit
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Pasi, Niharika. "Analysis of online news media through visualisation and text clustering." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-361562.

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Online news has grown in frequency and popularity as a convenient source of information for several years. A result of this drastic surge is the increased competition for viewer-ship and prolonged relevance of online news websites. Higher demands by internet audiences have led to the use of sensationalism such as ‘clickbait’ articles or ‘fake news’ to attract more viewers. The subsequent shift in the journalistic approach in new media opened new opportunities to study the behaviour and intent behind the news content. As news publications cater their news to a specific target audience, conclusi
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Calli, Cagatay. "Improving Search Result Clustering By Integrating Semantic Information From Wikipedia." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612554/index.pdf.

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Suffix Tree Clustering (STC) is a search result clustering (SRC) algorithm focused on generating overlapping clusters with meaningful labels in linear time. It showed the feasibility of SRC but in time, subsequent studies introduced description-first algorithms that generate better labels and achieve higher precision. Still, STC remained as the fastest SRC algorithm and there appeared studies concerned with different problems of STC. In this thesis, semantic relations between cluster labels and documents are exploited to filter out noisy labels and improve merging phase of STC. Wikipedia is us
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Katerattanakul, Nitsawan. "A pilot study in an application of text mining to learning system evaluation." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Katerattanakul_09007dcc807b614f.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).
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Pourvali, Mohsen <1984&gt. "Improving the quality of text clustering and cluster labeling." Doctoral thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/10311.

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The abundance of available electronic information is rapidly increasing with the advancements in digital processing. Furthermore, huge amounts of textual data have given rise to the need for efficient techniques that can organize the data in manageable forms. In order to tackle this challenge, clustering algorithms try to group automatically similar documents. While clustering plays a significant role that helps to categorize documents, it owes intrinsic limits when it comes to allowing human users to understand the content of documents at a deeper level. This is where cluster labeling techniq
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Hou, Jun. "Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79206/1/Jun_Hou_Thesis.pdf.

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This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the
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Zhu, Weizhong Allen Robert B. "Text clustering and active learning using a LSI subspace signature model and query expansion /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3077.

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Pratiffi, Michele. "Tecniche di text mining per l'autoorganizzazione della conoscenza." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6092/.

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L'elaborato ha come scopo l'analisi delle tecniche di Text Mining e la loro applicazione all'interno di processi per l'auto-organizzazione della conoscenza. La prima parte della tesi si concentra sul concetto del Text Mining. Viene fornita la sua definizione, i possibili campi di utilizzo, il processo di sviluppo che lo riguarda e vengono esposte le diverse tecniche di Text Mining. Si analizzano poi alcuni tools per il Text Mining e infine vengono presentati alcuni esempi pratici di utilizzo. Il macro-argomento che viene esposto successivamente riguarda TuCSoN, una infrastruttura per la coordi
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Books on the topic "Clustering of text information"

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Wu, Weili, Hui Xiong, and Shashi Shekhar. Clustering and Information Retrieval. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0227-8.

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Nedjah, Nadia, Luiza de Macedo Mourelle, Janusz Kacprzyk, Felipe M. G. França, and Alberto Ferreira de De Souza, eds. Intelligent Text Categorization and Clustering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85644-3.

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1969-, Srivastava Ashok, and Sahami Mehran, eds. Text mining: Classification, clustering, and applications. CRC Press, 2009.

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Acharya, Viral V. Endogenous information flows and the clustering of announcements. National Bureau of Economic Research, 2010.

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Acharya, Viral V. Endogenous information flows and the clustering of announcements. National Bureau of Economic Research, 2010.

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W, Berry Michael, Castellanos Malu, Society for Industrial and Applied Mathematics., Text Mining Workshop (5th : 2007 : Minneapolis, Minn.), and SIAM International Conference on Data Mining. (7th : 2007 : Minneapolis, Minn.), eds. Survey of text mining II: Clustering, classification, and retrieval. Springer, 2008.

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Hoffmann-Burchardi, Ulrike. Clustering of initial public offerings, information revelation and underpricing. London School of Economics, Financial Markets Group, 1999.

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Abualigah, Laith Mohammad Qasim. Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10674-4.

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W, Berry Michael, ed. Survey of text mining: Clustering, classification, and retrieval ; with 57 illustrations. Springer, 2004.

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T, Meadow Charles, Boyce Bert R, and Kraft Donald H, eds. Text information retrieval systems. 3rd ed. Academic, 2007.

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Book chapters on the topic "Clustering of text information"

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Wu, Junjie. "Information-Theoretic K-means for Text Clustering." In Advances in K-means Clustering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29807-3_4.

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Quaresma, Paulo, and Irene Pimenta Rodrigues. "Cooperative Information Retrieval Dialogues through Clustering." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45323-7_70.

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Gad, Walaa K., and Mohamed S. Kamel. "Enhancing Text Clustering Performance Using Semantic Similarity." In Enterprise Information Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01347-8_28.

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Pourvali, Mohsen, Salvatore Orlando, and Mehrad Gharagozloo. "Improving Clustering Quality by Automatic Text Summarization." In Information Retrieval Technology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28940-3_23.

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Rahimtoroghi, Elahe, and Azadeh Shakery. "Wikipedia-Based Smoothing for Enhancing Text Clustering." In Information Retrieval Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25631-8_30.

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Wang, Jianfeng, and Lina Ma. "Text Clustering Based on LSA-HGSOM." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23982-3_1.

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Hung, Ping Ju, Ping Yu Hsu, Ming Shien Cheng, and Chih Hao Wen. "Web Text Clustering with Dynamic Themes." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23982-3_16.

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Dev, Divya D., and Merlin Jebaruby. "Text Clustering Using Novel Hybrid Algorithm." In Intelligent Information and Database Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05476-6_2.

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Ribeiro, Marcelo N., Manoel J. R. Neto, and Ricardo B. C. Prudêncio. "Local Feature Selection in Text Clustering." In Advances in Neuro-Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03040-6_6.

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Shin, Kwangcheol, and Sangyong Han. "Fast Clustering Algorithm for Information Organization." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36456-0_69.

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Conference papers on the topic "Clustering of text information"

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Kamthawee, Krissanee, Can Udomcharoenchaikit, and Sarana Nutanong. "MIST: Mutual Information Maximization for Short Text Clustering." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.610.

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Malani, Pranav, and Prafulla Bafna. "Context-based Marwari text clustering." In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2024. https://doi.org/10.1109/spices62143.2024.10779675.

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Aggarwal, Charu C., Yuchen Zhao, and Philip S. Yu. "On Text Clustering with Side Information." In 2012 IEEE International Conference on Data Engineering (ICDE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icde.2012.111.

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Marcacini, Ricardo Marcondes, and Solange Oliveira Rezende. "Incremental hierarchical text clustering with privileged information." In DocEng '13: ACM Symposium on Document Engineering 2013. ACM, 2013. http://dx.doi.org/10.1145/2494266.2494296.

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Karuna, Evgeniy N., and Petr V. Sokolov. "Comparative Analysis of Text Information Clustering Methods." In 2021 XXIV International Conference on Soft Computing and Measurements (SCM). IEEE, 2021. http://dx.doi.org/10.1109/scm52931.2021.9507189.

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Jicinsky, Milan, and Jaroslav Marek. "Clustering analysis of phonetic and text feature vectors." In 2017 IEEE 14th International Scientific Conference on Informatics. IEEE, 2017. http://dx.doi.org/10.1109/informatics.2017.8327237.

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Thomas, Ramya Elizabeth, and Shamsuddin S. Khan. "Co-Clustering with Side Information for Text mining." In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE). IEEE, 2016. http://dx.doi.org/10.1109/sapience.2016.7684152.

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Aliwy, Ahmed H., Kadhim B. S. Aljanabi, and Huda A. Alameen. "Arabic text clustering technique to improve information retrieval." In 3RD INTERNATIONAL SCIENTIFIC CONFERENCE OF ALKAFEEL UNIVERSITY (ISCKU 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0066837.

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Wang, Ai, YaoDong Li, and Wei Wang. "Text Clustering Based on Key Phrases." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.1163.

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Yan, Xiaodong, Yuan Sun, xiaobing Zhao, and Guosheng Yang. "A Tibetan web Text Clustering model." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5690837.

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Reports on the topic "Clustering of text information"

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Seo, Young-Woo, and Katia Sycara. Text Clustering for Topic Detection. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada599196.

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Kirschenbaum, S. S. Information Clustering for Better Decision-Making. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada286292.

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Kanani, Pallika, and Andrew McCallum. Resource-Bounded Information Gathering for Correlation Clustering. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada464769.

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Acharya, Viral, Peter DeMarzo, and Ilan Kremer. Endogenous Information Flows and the Clustering of Announcements. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16485.

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Irwin, N. H., S. M. DeLand, and S. V. Crowder. Extraction of information from unstructured text. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/148697.

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Kostoff, Ronald N. Science and Technology Text Mining: Origins of Database Tomography and Multi-Word Phrase Clustering. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416268.

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Irwin, N. H. Domain-independent information extraction in unstructured text. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/378821.

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Strzalkowski, Tomek. Robust Text Processing in Automated Information Retrieval. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada460240.

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Braun, Ronald. Ontology-Based Information Extraction from Free-Form Text. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383044.

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Hobbs, Jerry R., Douglas Appelt Sr, John S. Bear, David Israel Sr, and W. M. Tyson. FASTUS: A System for Extracting Information from Natural-Language Text. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada259435.

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