Relevant bibliographies by topics / Text Clustering Techniques

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

Academic literature on the topic 'Text Clustering Techniques'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Text Clustering Techniques"

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Kumar, Mukesh, and Amandeep Verma. "Text Clustering Techniques A Review." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 1091–99. http://dx.doi.org/10.26438/ijcse/v6i6.10911099.

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Mohit, *. A. Charan Kumari Meghna Sharma. "TEXT CLUSTERING TECHNIQUES: A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 248–56. https://doi.org/10.5281/zenodo.573535.

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The advancements in the fields of mobile computing, grid computing, cloud computing, Internet of Things and primarily due to the availability of internet in the hand-held devices were the vital key factors in the growth of large amounts of data. The main challenge is to organize this big data in a structured manner that helps to derive new insights, predictive analysis and to find trends, patterns and their correlations. One of the solutions is to cluster the text, a significant technique of data mining. This paper investigates various techniques experimented in text clustering. It also descri
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Upadhye, Akshata. "A Survey of Text Clustering Techniques: Algorithms, Applications, and Challenges." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 1749–52. http://dx.doi.org/10.21275/sr24304163737.

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S. Patil, Ratna, and B. S. Chordia. "Mining Text Data using different Text Clustering Techniques." International Journal of Computer Trends and Technology 43, no. 2 (2017): 87–93. http://dx.doi.org/10.14445/22312803/ijctt-v43p113.

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Sharma, Saurabh, and Vishal Gupta. "Recent Developments in Text Clustering Techniques." International Journal of Computer Applications 37, no. 6 (2012): 14–19. http://dx.doi.org/10.5120/4611-6604.

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Liu, Wei, and Wilson Wong. "Web service clustering using text mining techniques." International Journal of Agent-Oriented Software Engineering 3, no. 1 (2009): 6. http://dx.doi.org/10.1504/ijaose.2009.022944.

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Jalal, Ahmed Adeeb, and Basheer Husham Ali. "Text documents clustering using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 664. http://dx.doi.org/10.11591/ijece.v11i1.pp664-670.

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Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes ma
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Ahmed, Adeeb Jalal, and Husham Ali Basheer. "Text documents clustering using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 664–70. https://doi.org/10.11591/ijece.v11i1.pp664-670.

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Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes ma
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VarshaC., Pande*1 Dr. Harshala B. Pethe2 &. Dr. Abha. S. Khandelwal3. "CLUSTERING AND CLASSIFICATION TECHNIQUES USING TEXT MINING." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [NC-Rase 18] (November 12, 2018): 9–15. https://doi.org/10.5281/zenodo.1483957.

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The text is nothing but the combination of characters. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several methods have been proposed for analyzing such texts and extracting information.Data mining, a specific area named text mining is used to classify the huge semi structured or unstructured data needs proper clustering. Maximum text documents involves fast retrieval of information, arrangement of documents, exploring of information from the documents. Declaration of text input data and classification of the documents is a complex process. Te
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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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Dissertations / Theses on the topic "Text Clustering Techniques"

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Ranby, Erik. "A comparison of clustering techniques for short social text messages." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196735.

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The amount of social text messages authored each day is huge and the information contained within is potentially very valuable. Software that can cluster and thereby help analyze these messages would consequently be helpful. This thesis explores several ways of clustering social text messages. Two algorithms and several setups with these algorithms have been tested and evaluated with the same data as input. Based on these evaluations, a comparison has been conducted in order to answer the question which algorithm setup is best suited for the task. The two clustering algorithms that have been t
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Zhu, Dengya. "Improving the relevance of web search results by combining web snippet categorization, clustering and personalization." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/326.

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Web search results are far from perfect due to the polysemous and synonymous characteristics of nature languages, information overload as the results of information explosion on the Web, and the flat list, “one size fits all” strategies of search engines to present search results without concentrating on user personal information needs.Re-organizing Web search results, or Web snippets, by means of text categorization and clustering are two dominant approaches to attack the issues above. Text categorization uses a collection of labeled documents to train a classifier which can then predict labe
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DI, CORSO EVELINA. "Text miner's little helper: scalable self-tuning methodologies for knowledge exploration." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2738395.

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Tarafdar, Arundhati. "Wordspotting from multilingual and stylistic documents." Thesis, Tours, 2017. http://www.theses.fr/2017TOUR4022/document.

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Les outils et méthodes d’analyse d’images de documents (DIA) donnent aujourd’hui la possibilité de faire des recherches par mots-clés dans des bases d’images de documents alors même qu’aucune transcription n’est disponible. Dans ce contexte, beaucoup de travaux ont déjà été réalisés sur les OCR ainsi que sur des systèmes de repérage de mots (spotting) dédiés à des documents textuels avec une mise en page simple. En revanche, très peu d’approches ont été étudiées pour faire de la recherche dans des documents contenant du texte multi-orienté et multi-échelle, comme dans les documents graphiques.
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Peng, Jyun-Fan, and 彭俊凡. "An analysis of biomedical literatures based on document clustering and text mining techniques." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/a962m7.

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碩士<br>臺北市立大學<br>數學系數學教育碩士班<br>105<br>This research use text mining technique to analyze the biomedical literatures which are the National Health Insurance literatures included in PubMed database in recent decade. The digital archives technique gets more and more popular and advanced, so that people can get the digital archives whenever and wherever they want. Even so, we still rely on the human ability to process the literature arrangement, which is inefficient. Therefore, the efficient method to get the useful information is the important issue that scholars want to figure out. This research
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Hsueh, Hung Yeh, and 薛弘業. "A Study of Stock Price Prediction with Text Mining, Classification and Clustering Techniques in Taiwan Stock Market." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/98972323381665117777.

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碩士<br>國立政治大學<br>資訊管理研究所<br>101<br>This study investigated the relation that the stock news effect on Taiwan Stock Market. Through collected the historical transaction data and stock news from July, 2012 to May, 2013, and use text mining、kNN Classification and 2-Way kNN Clustering technique analyzing the stock news, build a forecast model to analyze the degree of news effect on the stock price, and find the relation between the cluster which has great degree and the reversal points of stock price. The result shows that using the change range and Technical Indicator rise classification’s accura
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(6630578), Yellamraju Tarun. "n-TARP: A Random Projection based Method for Supervised and Unsupervised Machine Learning in High-dimensions with Application to Educational Data Analysis." Thesis, 2019.

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Analyzing the structure of a dataset is a challenging problem in high-dimensions as the volume of the space increases at an exponential rate and typically, data becomes sparse in this high-dimensional space. This poses a significant challenge to machine learning methods which rely on exploiting structures underlying data to make meaningful inferences. This dissertation proposes the <i>n</i>-TARP method as a building block for high-dimensional data analysis, in both supervised and unsupervised scenarios.<div><br></div><div>The basic element, <i>n</i>-TARP, consists of a random projection framew
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Books on the topic "Text Clustering Techniques"

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Peebles, P. J. E. The Large-Scale Structure of the Universe. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691209838.001.0001.

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An instant landmark on its publication, this book remains the essential introduction to this vital area of research. Written by one of the world's most esteemed theoretical cosmologists, it provides an invaluable historical introduction to the subject, and an enduring overview of key methods, statistical measures, and techniques for dealing with cosmic evolution. With characteristic clarity and insight, the author focuses on the largest known structures — galaxy clusters — weighing the empirical evidence of the nature of clustering and the theories of how it evolves in an expanding universe. A
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Book chapters on the topic "Text Clustering Techniques"

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Bader, Brett W., and Peter A. Chew. "Algebraic Techniques for Multilingual Document Clustering." In Text Mining. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470689646.ch2.

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Huang, Bing Quan, Y. B. Zhang, and M. T. Kechadi. "Preprocessing Techniques for Online Handwriting Recognition." In Intelligent Text Categorization and Clustering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85644-3_2.

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Singh, Lovedeep. "Clustering Text: A Comparison Between Available Text Vectorization Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1249-7_3.

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Pham, Hanh. "An Interval-based Method for Text Clustering." In Advanced Techniques in Computing Sciences and Software Engineering. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3660-5_99.

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Ahmed, Zaheeruddin, Sonu Mittal, and Harvir Singh. "Text Clustering Techniques for Voice of Customer Analysis." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6981-8_57.

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Yadav, Sushant, and Archana Singhal. "Comparative Study of Clustering Techniques for Extractive Text Summarization." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3292-0_26.

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Huang, Cheng-Chia, Atsushi Nara, Joseph Gibbons, and Ming-Hsiang Tsou. "Exploring Gentrification Through Social Media Data and Text Clustering Techniques." In Human Dynamics in Smart Cities. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83010-6_13.

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Abualigah, Laith Mohammad, Ahamad Tajudin Khader, and Essam Said Hanandeh. "A Novel Weighting Scheme Applied to Improve the Text Document Clustering Techniques." In Innovative Computing, Optimization and Its Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66984-7_18.

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Shotorbani, Peyman Yazdizadeh, Farhad Ameri, Boonserm Kulvatunyou, and Nenad Ivezic. "A Hybrid Method for Manufacturing Text Mining Based on Document Clustering and Topic Modeling Techniques." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51133-7_91.

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Mansour, Ali, Juman Mohammad, Yury Kravchenko, Daniil Kravchenko, and Nemury Silega. "Harnessing Key Phrases in Constructing a Concept-Based Semantic Representation of Text Using Clustering Techniques." In Progress in Artificial Intelligence and Pattern Recognition. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49552-6_17.

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Conference papers on the topic "Text Clustering Techniques"

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Rathnayake, R. M. P. P., and P. P. G. Dinesh Asanka. "Intelligent Document Clustering for Sinhala Text: Combining LLM-based NER with Fuzzy Clustering Techniques." In 2025 5th International Conference on Advanced Research in Computing (ICARC). IEEE, 2025. https://doi.org/10.1109/icarc64760.2025.10963238.

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Karim, Abdelrhman A., Manar A. Ibrahim, Mahmoud M. Hosny, et al. "A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-Means Clustering, and PageRank Algorithm." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783534.

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Kaur, Manpreet, and Meenakshi Bansal. "Text classification using clustering techniques and P.C.A." In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2016. http://dx.doi.org/10.1109/pdgc.2016.7913201.

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Kadhim, Ammar Ismael, Yu-N. Cheah, and Nurul Hashimah Ahamed. "Text Document Preprocessing and Dimension Reduction Techniques for Text Document Clustering." In 2014 Artificial Intelligence with Applications in Engineering and Technology (ICAIET). IEEE, 2014. http://dx.doi.org/10.1109/icaiet.2014.21.

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Rangrej, Aniket, Sayali Kulkarni, and Ashish V. Tendulkar. "Comparative study of clustering techniques for short text documents." In the 20th international conference companion. ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963249.

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Bello-Orgaz, Gema, and David Camacho. "Comparative study of text clustering techniques in virtual worlds." In the 3rd International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2479787.2479818.

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Shetty, Krithi, and Jagadish S. Kallimani. "Automatic extractive text summarization using K-means clustering." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284627.

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Kumar, Manoj, D. K. Yadav, and Vijay Kumar Gupta. "Frequent term based text document clustering: A new approach." In 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI). IEEE, 2015. http://dx.doi.org/10.1109/icscti.2015.7489630.

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Mansour, Ali, Juman Mohammad, and Yury Kravchenko. "Text Vectorization Method Based on Concept Mining Using Clustering Techniques." In 2022 VI International Conference on Information Technologies in Engineering Education (Inforino). IEEE, 2022. http://dx.doi.org/10.1109/inforino53888.2022.9782908.

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Kumbhar, Rutuja, Snehal Mhamane, Harshada Patil, Sukruta Patil, and Shubhangi Kale. "Text Document Clustering Using K-means Algorithm with Dimension Reduction Techniques." In 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. http://dx.doi.org/10.1109/icces48766.2020.9137928.

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