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Статті в журналах з теми "Textual data-mining"
Yasuda, Akio. "Reviewing "Text Mining": Textual Data Mining." IEEJ Transactions on Electronics, Information and Systems 125, no. 5 (2005): 682–89. http://dx.doi.org/10.1541/ieejeiss.125.682.
Повний текст джерелаRaiyani, Ronak S., Dr Bankim Radadiya, and Dr Satish Thumar. "Analyzing, Developing and Implementing Data Mining Techniques on Databases, Web Contents and Textual Data." Paripex - Indian Journal Of Research 2, no. 3 (January 15, 2012): 48–50. http://dx.doi.org/10.15373/22501991/mar2013/18.
Повний текст джерелаYassir, Ali Hameed, Ali A. Mohammed, Adel Abdul-Jabbar Alkhazraji, Mustafa Emad Hameed, Mohammed Saad Talib, and Mohanad Faeq Ali. "Sentimental classification analysis of polarity multi-view textual data using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5526. http://dx.doi.org/10.11591/ijece.v10i5.pp5526-5534.
Повний текст джерелаJayasudha, J., and A. Christina Esther. "Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 41–45. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1961.
Повний текст джерелаEltaher, Mohammed, and Jeongkyu Lee. "Social User Mining." International Journal of Multimedia Data Engineering and Management 4, no. 4 (October 2013): 58–70. http://dx.doi.org/10.4018/ijmdem.2013100104.
Повний текст джерелаDavahli, Mohammad Reza, Waldemar Karwowski, Edgar Gutierrez, Krzysztof Fiok, Grzegorz Wróbel, Redha Taiar, and Tareq Ahram. "Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data." Symmetry 12, no. 11 (November 19, 2020): 1902. http://dx.doi.org/10.3390/sym12111902.
Повний текст джерелаHOLZMAN, LARS E., TODD A. FISHER, LEON M. GALITSKY, APRIL KONTOSTATHIS, and WILLIAM M. POTTENGER. "A SOFTWARE INFRASTRUCTURE FOR RESEARCH IN TEXTUAL DATA MINING." International Journal on Artificial Intelligence Tools 13, no. 04 (December 2004): 829–49. http://dx.doi.org/10.1142/s0218213004001843.
Повний текст джерелаChen, Pei Bin, Lan Hu, Hui Yang, Xiang Feng Xue, Chuan Xu Liu, and Xin Jian Li. "Target Value Analysis Based on Data Mining Technology." Applied Mechanics and Materials 602-605 (August 2014): 3096–99. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3096.
Повний текст джерелаUr-Rahman, Nadeem. "Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study." European Scientific Journal, ESJ 13, no. 21 (July 31, 2017): 429. http://dx.doi.org/10.19044/esj.2017.v13n21p429.
Повний текст джерелаAlguliev, Rasim M., Ramiz M. Aliguliyev, and Saadat A. Nazirova. "Classification of Textual E-Mail Spam Using Data Mining Techniques." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/416308.
Повний текст джерелаДисертації з теми "Textual data-mining"
Zhou, Wubai. "Data Mining Techniques to Understand Textual Data." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3493.
Повний текст джерелаUr-Rahman, Nadeem. "Textual data mining applications for industrial knowledge management solutions." Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6373.
Повний текст джерелаKubalík, Jakub. "Mining of Textual Data from the Web for Speech Recognition." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237170.
Повний текст джерелаKalledat, Tobias. "Tracking domain knowledge based on segmented textual sources." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2009. http://dx.doi.org/10.18452/15925.
Повний текст джерелаThe research work available here has the goal of analysing the influence of pre-processing on the results of the generation of knowledge and of giving concrete recommendations for action for suitable pre-processing of text corpora in TDM. The research introduced here focuses on the extraction and tracking of concepts within certain knowledge domains using an approach of horizontally (timeline) and vertically (persistence of terms) segmenting of corpora. The result is a set of segmented corpora according to the timeline. Within each timeline segment clusters of concepts can be built according to their persistence quality in relation to each single time-based corpus segment and to the whole corpus. Based on a simple frequency measure it can be shown that only the statistical quality of a single corpus allows measuring the pre-processing quality. It is not necessary to use comparison corpora. The time series of the frequency measure have significant negative correlations between the two clusters of concepts that occur permanently and others that vary within an optimal pre-processed corpus. This was found to be the opposite in every other test set that was pre-processed with lower quality. The most frequent terms were grouped into concepts by the use of domain-specific taxonomies. A significant negative correlation was found between the time series of different terms per yearly corpus segments and the terms assigned to taxonomy for corpora with high quality level of pre-processing. A semantic analysis based on a simple TDM method with significant frequency threshold measures resulted in significant different knowledge extracted from corpora with different qualities of pre-processing. With measures introduced in this research it is possible to measure the quality of applied taxonomy. Rules for the measuring of corpus as well as taxonomy quality were derived from these results and advice suggested for the appropriate level of pre-processing.
元吉, 忠寛, та Tadahiro MOTOYOSHI. "災害のイマジネーション力に関する探索的研究 - 大学生の想像力と阪神淡路大震災の事例との比較 -". 名古屋大学大学院教育発達科学研究科, 2006. http://hdl.handle.net/2237/9454.
Повний текст джерелаSpiegler, Sebastian R. "Comparative study of clustering algorithms on textual databases : clustering of curricula vitae into comptency-based groups to support knowledge management /." Saarbrücken : VDM Verl. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3035354&prov=M&dok_var=1&dok_ext=htm.
Повний текст джерелаNieto, Erick Mauricio Gómez. "Projeção multidimensional aplicada a visualização de resultados de busca textual." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122012-105730/.
Повний текст джерелаInternet users are very familiar with the results of a search query displayed as a ranked list of snippets. Each textual snippet shows a content summary of the referred document (or web page) and a link to it. This display has many advantages, e.g., it affords easy navigation and is straightforward to interpret. Nonetheless, any user of search engines could possibly report some experience of disappointment with this metaphor. Indeed, it has limitations in particular situations, as it fails to provide an overview of the document collection retrieved. Moreover, depending on the nature of the query - e.g., it may be too general, or ambiguous, or ill expressed - the desired information may be poorly ranked, or results may contemplate varied topics. Several search tasks would be easier if users were shown an overview of the returned documents, organized so as to reflect how related they are, content-wise. We propose a visualization technique to display the results of web queries aimed at overcoming such limitations. It combines the neighborhood preservation capability of multidimensional projections with the familiar snippet-based representation by employing a multidimensional projection to derive two-dimensional layouts of the query search results that preserve text similarity relations, or neighborhoods. Similarity is computed by applying the cosine similarity over a bag-of-words vector representation of collection built from the snippets. If the snippets are displayed directly according to the derived layout they will overlap considerably, producing a poor visualization. We overcome this problem by defining an energy functional that considers both the overlapping amongst snippets and the preservation of the neighborhood structure as given in vii the projected layout. Minimizing this energy functional provides a neighborhood preserving two-dimensional arrangement of the textual snippets with minimum overlap. The resulting visualization conveys both a global view of the query results and visual groupings that reflect related results, as illustrated in several examples shown
Fabbri, Renato. "Topological stability and textual differentiation in human interaction networks: statistical analysis, visualization and linked data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092017-154706/.
Повний текст джерелаEste trabalho relata propriedades topológicas estáveis (ou invariantes) e diferenciação textual em redes de interação humana, com referências derivadas de listas públicas de e-mail. A atividade ao longo do tempo e a topologia foram observadas em instantâneos ao longo de uma linha do tempo e em diferentes escalas. A análise mostra que a atividade é praticamente a mesma para todas as redes em escalas temporais de segundos a meses. As componentes principais dos participantes no espaço das métricas topológicas mantêm-se praticamente inalteradas quando diferentes conjuntos de mensagens são considerados. A atividade dos participantes segue o esperado perfil livre de escala, produzindo, assim, as classes de vértices dos hubs, dos intermediários e dos periféricos em comparação com o modelo Erdös-Rényi. Os tamanhos relativos destes três setores são essencialmente os mesmos para todas as listas de e-mail e ao longo do tempo. Normalmente, 3-12% dos vértices são hubs, 15-45% são intermediários e 44-81% são vértices periféricos. Os textos de cada um destes setores são considerados muito diferentes através de uma adaptação dos testes de Kolmogorov-Smirnov. Estas propriedades são consistentes com a literatura e podem ser gerais para redes de interação humana, o que tem implicações importantes para o estabelecimento de uma tipologia dos participantes com base em critérios quantitativos. De modo a guiar e apoiar esta pesquisa, também desenvolvemos um método de visualização para redes dinâmicas através de animações. Para facilitar a verificação e passos seguintes nas análises, fornecemos uma representação em dados ligados dos dados relacionados aos nossos resultados.
Mendes, MarÃlia Soares. "MALTU - model for evaluation of interaction in social systems from the Users Textual Language." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14296.
Повний текст джерелаA Ãrea de InteraÃÃo Humano-Computador (IHC) tem sugerido muitas formas para avaliar sistemas a fim de melhorar sua usabilidade e a eXperiÃncia do UsuÃrio (UX). O surgimento da web 2.0 permitiu o desenvolvimento de aplicaÃÃes marcadas pela colaboraÃÃo, comunicaÃÃo e interatividade entre seus usuÃrios de uma forma e em uma escala nunca antes observadas. Sistemas Sociais (SS) (e.g., Twitter, Facebook, MySpace, LinkedIn etc.) sÃo exemplos dessas aplicaÃÃes e possuem caracterÃsticas como: frequente troca de mensagens e expressÃo de sentimentos de forma espontÃnea. As oportunidades e os desafios trazidos por esses tipos de aplicaÃÃes exigem que os mÃtodos tradicionais de avaliaÃÃo sejam repensados, considerando essas novas caracterÃsticas. Por exemplo, as postagens dos usuÃrios em SS revelam suas opiniÃes sobre diversos assuntos, inclusive sobre o que eles pensam do sistema em uso. Esta tese procura testar a hipÃtese de que as postagens dos usuÃrios em SS fornecem dados relevantes para avaliaÃÃo da Usabilidade e da UX (UUX) em SS. Durante as pesquisas realizadas na literatura, nÃo foi identificado nenhum modelo de avaliaÃÃo que tenha direcionado seu foco na coleta e anÃlise das postagens dos usuÃrios a fim de avaliar a UUX de um sistema em uso. Sendo assim, este estudo propÃe o MALTU â Modelo para AvaliaÃÃo da interaÃÃo em sistemas sociais a partir da Linguagem Textual do UsuÃrio. A fim de fornecer bases para o desenvolvimento do modelo proposto, foram realizados estudos de como os usuÃrios expressam suas opiniÃes sobre o sistema em lÃngua natural. Foram extraÃdas postagens de usuÃrios de quatro SS de contextos distintos. Tais postagens foram classificadas por especialistas de IHC, estudadas e processadas utilizando tÃcnicas de Processamento da Linguagem Natural (PLN) e mineraÃÃo de dados e, analisadas a fim da obtenÃÃo de um modelo genÃrico. O MALTU foi aplicado em dois SS: um de entretenimento e um SS educativo. Os resultados mostram que à possÃvel avaliar um sistema a partir das postagens dos usuÃrios em SS. Tais avaliaÃÃes sÃo auxiliadas por padrÃes de extraÃÃo relacionados ao uso, aos tipos de postagens e Ãs metas de IHC utilizadas na avaliaÃÃo do sistema.
Kamenieva, Iryna. "Research Ontology Data Models for Data and Metadata Exchange Repository." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-6351.
Повний текст джерелаFor researches in the field of the data mining and machine learning the necessary condition is an availability of various input data set. Now researchers create the databases of such sets. Examples of the following systems are: The UCI Machine Learning Repository, Data Envelopment Analysis Dataset Repository, XMLData Repository, Frequent Itemset Mining Dataset Repository. Along with above specified statistical repositories, the whole pleiad from simple filestores to specialized repositories can be used by researchers during solution of applied tasks, researches of own algorithms and scientific problems. It would seem, a single complexity for the user will be search and direct understanding of structure of so separated storages of the information. However detailed research of such repositories leads us to comprehension of deeper problems existing in usage of data. In particular a complete mismatch and rigidity of data files structure with SDMX - Statistical Data and Metadata Exchange - standard and structure used by many European organizations, impossibility of preliminary data origination to the concrete applied task, lack of data usage history for those or other scientific and applied tasks.
Now there are lots of methods of data miming, as well as quantities of data stored in various repositories. In repositories there are no methods of DM (data miming) and moreover, methods are not linked to application areas. An essential problem is subject domain link (problem domain), methods of DM and datasets for an appropriate method. Therefore in this work we consider the building problem of ontological models of DM methods, interaction description of methods of data corresponding to them from repositories and intelligent agents allowing the statistical repository user to choose the appropriate method and data corresponding to the solved task. In this work the system structure is offered, the intelligent search agent on ontological model of DM methods considering the personal inquiries of the user is realized.
For implementation of an intelligent data and metadata exchange repository the agent oriented approach has been selected. The model uses the service oriented architecture. Here is used the cross platform programming language Java, multi-agent platform Jadex, database server Oracle Spatial 10g, and also the development environment for ontological models - Protégé Version 3.4.
Книги з теми "Textual data-mining"
Inmon, William H. Tapping into unstructured data: Integrating unstructured data and textual analytics into business intelligence. Upper Saddle River, NJ: Prentice Hall, 2008.
Знайти повний текст джерелаPoibeau, Thierry. Traitement automatique du contenu textuel. Paris: Hermès science-Lavoisier, 2011.
Знайти повний текст джерелаInmon, Bill. Turning Text into Gold: Taxonomies and Textual Analytics. Technics Publications, LLC, 2017.
Знайти повний текст джерелаInmon, William, and Anthony Nesavich. Tapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence. Pearson Education, 2007.
Знайти повний текст джерелаTapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence. Prentice Hall PTR, 2007.
Знайти повний текст джерелаЧастини книг з теми "Textual data-mining"
Poon, Leonard K. M., Chun Fai Leung, and Nevin L. Zhang. "Mining Textual Reviews with Hierarchical Latent Tree Analysis." In Data Mining and Big Data, 401–8. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_40.
Повний текст джерелаNguyen, Thin, Svetha Venkatesh, and Dinh Phung. "Textual Cues for Online Depression in Community and Personal Settings." In Advanced Data Mining and Applications, 19–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_2.
Повний текст джерелаKurach, Karol, Krzysztof Pawłowski, Łukasz Romaszko, Marcin Tatjewski, Andrzej Janusz, and Hung Son Nguyen. "An Ensemble Approach to Multi-label Classification of Textual Data." In Advanced Data Mining and Applications, 306–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_26.
Повний текст джерелаBalbi, Simona, and Emilio Meglio. "Contributions of Textual Data Analysis to Text Retrieval." In Classification, Clustering, and Data Mining Applications, 511–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_48.
Повний текст джерелаCho, Vincent, and Beat Wüthrich. "Combining Forecasts from Multiple Textual Data Sources." In Methodologies for Knowledge Discovery and Data Mining, 174–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_24.
Повний текст джерелаGalanopoulos, Damianos, Milan Dojchinovski, Krishna Chandramouli, Tomáš Kliegr, and Vasileios Mezaris. "Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video." In Multimedia Data Mining and Analytics, 295–310. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_13.
Повний текст джерелаTakasu, Atsuhiro. "A Sequence Labeling Method Using Syntactical and Textual Patterns for Record Linkage." In Pattern Recognition and Data Mining, 199–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_22.
Повний текст джерелаSinghal, Mayank, and Suman Banerjee. "Group Trip Planning Queries on Road Networks Using Geo-Tagged Textual Information." In Advanced Data Mining and Applications, 243–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95405-5_18.
Повний текст джерелаRajman, Martin, and Romaric Besançon. "Text Mining - Knowledge extraction from unstructured textual data." In Studies in Classification, Data Analysis, and Knowledge Organization, 473–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_64.
Повний текст джерелаLai⋆, Kwok-Yin, and Wai Lam. "Meta-learning Models for Automatic Textual Document Categorization." In Advances in Knowledge Discovery and Data Mining, 78–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_11.
Повний текст джерелаТези доповідей конференцій з теми "Textual data-mining"
Caputo, G. M., and N. F. F. Ebecken. "Computational system for the textual processing of industrial patents." In DATA MINING AND MIS 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/data060171.
Повний текст джерелаFize, Jacques, Mathieu Roche, and Maguelonne Teisseire. "Matching Heterogeneous Textual Data Using Spatial Features." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00197.
Повний текст джерелаTan, Pang-Ning, Hannah Blau, Steve Harp, and Robert Goldman. "Textual data mining of service center call records." In the sixth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/347090.347177.
Повний текст джерелаMichalenko, Joshua J., Andrew S. Lan, and Richard G. Baraniuk. "Data-Mining Textual Responses to Uncover Misconception Patterns." In L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3051457.3053996.
Повний текст джерелаNath, Devjyoti, Anirban Roy, Sumitra Kumari Shaw, Amlan Ghorai, and Shanta Phani. "Textual Lyrics Based Emotion Analysis of Bengali Songs." In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00015.
Повний текст джерелаXu, Jia. "Joint Visual and Textual Mining on Social Media." In 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.114.
Повний текст джерелаAkhtar, Nadeem, Bushra Siddique, and Rounaque Afroz. "Visual and textual summarization of webpages." In 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC). IEEE, 2014. http://dx.doi.org/10.1109/icdmic.2014.6954267.
Повний текст джерелаRoos, Teemu, and Yuan Zou. "Analysis of Textual Variation by Latent Tree Structures." In 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.24.
Повний текст джерелаNeri, Federico, and Paolo Geraci. "Mining Textual Data to Boost Information Access in OSINT." In 2009 13th International Conference Information Visualisation, IV. IEEE, 2009. http://dx.doi.org/10.1109/iv.2009.99.
Повний текст джерелаHristidis, Vagelis, Oscar Valdivia, Michail Vlachos, and Philip S. Yu. "A System for Keyword Search on Textual Streams." In Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.52.
Повний текст джерелаЗвіти організацій з теми "Textual data-mining"
Dooley, Kevin, Steven Corman, and Dan Ballard. Centering Resonance Analysis: A Superior Data Mining Algorithm for Textual Data Streams. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422048.
Повний текст джерелаNeyedley, K., J. J. Hanley, Z. Zajacz, and M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.
Повний текст джерелаNeyedley, K., J. J. Hanley, P. Mercier-Langevin, and M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.
Повний текст джерела