Literatura académica sobre el tema "Data mining – social aspects"
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Artículos de revistas sobre el tema "Data mining – social aspects"
Eltaher, Mohammed y Jeongkyu Lee. "Social User Mining". International Journal of Multimedia Data Engineering and Management 4, n.º 4 (octubre de 2013): 58–70. http://dx.doi.org/10.4018/ijmdem.2013100104.
Texto completoAl-Saggaf, Yeslam y Md Zahidul Islam. "Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem". Science and Engineering Ethics 21, n.º 4 (12 de junio de 2014): 941–66. http://dx.doi.org/10.1007/s11948-014-9564-6.
Texto completoWang, Chen-Ya y Hsia-Ching Chang. "Choice Modeling of Enterprise Social Media Adoptions". International Journal of E-Adoption 11, n.º 1 (enero de 2019): 12–24. http://dx.doi.org/10.4018/ijea.2019010102.
Texto completoMir, J., A. Mahmood y S. Khatoon. "Aspect Βased Classification Model for Social Reviews". Engineering, Technology & Applied Science Research 7, n.º 6 (18 de diciembre de 2017): 2296–302. http://dx.doi.org/10.48084/etasr.1578.
Texto completoLICCUD-AMBEGUIA, FLORENCE H. "ENHANCING MINING COMMUNITY SERVICES THROUGH CORPORATE SOCIAL RESPONSIBILITY AND SOCIAL DEVELOPMENT MANAGEMENT STRATEGIES". Cognizance Journal of Multidisciplinary Studies 3, n.º 11 (30 de noviembre de 2023): 58–97. http://dx.doi.org/10.47760/cognizance.2023.v03i11.006.
Texto completoDahish, Zahra y Shah J. Miah. "EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE". International Journal on Soft Computing 14, n.º 1 (27 de febrero de 2023): 1–12. http://dx.doi.org/10.5121/ijsc.2023.14101.
Texto completoHuang, Chi-Yo, Chia-Lee Yang y Yi-Hao Hsiao. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods". Mathematics 9, n.º 17 (25 de agosto de 2021): 2041. http://dx.doi.org/10.3390/math9172041.
Texto completoTrandafili, Evis y Marenglen Biba. "A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks". International Journal of E-Business Research 9, n.º 1 (enero de 2013): 36–53. http://dx.doi.org/10.4018/jebr.2013010103.
Texto completoCAO, LONGBING y CHENGQI ZHANG. "THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING". International Journal of Pattern Recognition and Artificial Intelligence 21, n.º 04 (junio de 2007): 677–92. http://dx.doi.org/10.1142/s0218001407005612.
Texto completoSeadle, Michael S. "Managing and mining historical research data". Library Hi Tech 34, n.º 1 (21 de marzo de 2016): 172–79. http://dx.doi.org/10.1108/lht-09-2015-0086.
Texto completoTesis sobre el tema "Data mining – social aspects"
Chen, Weidong. "Discovering communities by information diffusion and link density propagation". HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1422.
Texto completoNguyen, Ngoc Buu Cat. "Data Mining in Knowledge Management Processes: Developing an Implementing Framework". Thesis, Umeå universitet, Institutionen för informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149668.
Texto completoYang, Shuang-Hong. "Predictive models for online human activities". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.
Texto completoCai, Zhongming. "Technical aspects of data mining". Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.
Texto completoEriksson, Jesper y Samuel Björeqvist. "Datadriven Innovation : En komparativ studie om dataanalysmetoder och verktyg för små företag". Thesis, Umeå universitet, Institutionen för informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149865.
Texto completoWang, Guan. "Graph-Based Approach on Social Data Mining". Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.
Texto completoPowered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.
Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.
The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.
Ip, Lai Cheng. "Mining on social network community for marketing". Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.
Texto completoCosta, Alceu Ferraz. "Mining User Activity Data in Social Media Services". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11092017-151000/.
Texto completoO impacto dos serviços de mídia social em nossa sociedade é crescente. Indivíduos frequentemente utilizam mídias sociais para obter notícias, decidir quais os produtos comprar ou para se comunicar com amigos. Como consequência da adoção generalizada de mídias sociais, um grande volume de dados sobre como os usuários se comportam é gerado diariamente e armazenado em grandes bancos de dados. Aprender a analisar e extrair conhecimentos úteis a partir destes dados tem uma série de potenciais aplicações. Por exemplo, um entendimento mais detalhado sobre como usuários legítimos interagem com serviços de mídia social poderia ser explorado para projetar métodos mais precisos de detecção de spam e fraude. Esta pesquisa de doutorado baseia-se na seguinte hipótese: dados gerados por usuários de mídia social apresentam padrões que podem ser explorados para melhorar a eficácia de tarefas como previsão e modelagem no domínio das mídias sociais. Para validar esta hipótese, foram projetados métodos de mineração de dados adaptados aos dados de mídia social. As principais contribuições desta pesquisa de doutorado podem ser divididas em três partes. Primeiro, foi desenvolvido o Act-M, um modelo matemático que descreve o tempo das ações dos usuários. O autor demonstrou que o Act-M pode ser usado para detectar automaticamente bots entre usuários de mídia social com base apenas nos dados de tempo. A segunda contribuição desta tese é o VnC (Vote-and- Comment), um modelo que explica como o volume de diferentes tipos de interações de usuário evolui ao longo do tempo quando um conteúdo é submetido a um serviço de mídia social. Além de descrever precisamente os dados reais, o VnC é útil, pois pode ser empregado para prever o número de interações recebidas por determinado conteúdo de mídia social. Por fim, nossa terceira contribuição é o método MFS-Map. O MFS-Map fornece automaticamente anotações textuais para imagens de mídias sociais, combinando eficientemente características visuais e de metadados das imagens. As contribuições deste doutorado foram validadas utilizando dados reais de diversos serviços de mídia social. Os experimentos mostraram que os modelos Act-M e VnC forneceram um ajuste mais preciso aos dados quando comparados, respectivamente, a modelos existentes para dinâmica de comunicação e difusão de informação. O MFS-Map obteve precisão superior e tempo de execução reduzido quando comparado com outros métodos amplamente utilizados para anotação de imagens.
Meneghello, James. "A scalable framework for integrated social data mining". Thesis, Meneghello, James (2017) A scalable framework for integrated social data mining. PhD thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/36690/.
Texto completoAlsaleh, Slah. "Recommending people in social networks using data mining". Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61736/1/Slah_Alsaleh_Thesis.pdf.
Texto completoLibros sobre el tema "Data mining – social aspects"
1954-, Eyob Ephrem, ed. Social implications of data mining and information privacy: Interdisciplinary frameworks and solutions. Hershey PA: Information Science Reference, 2009.
Buscar texto completoAbraham, Ajith. Computational Social Networks: Mining and Visualization. London: Springer London, 2012.
Buscar texto completoBoellstorff, Tom y Bill Maurer. Data, now bigger and better! Chicago: Prickly Paradigm Press, 2015.
Buscar texto completoEric, Hunter. The Sherlock syndrome: Strategic success through big data and the Darwinian disruption. London: Ark Group, 2014.
Buscar texto completoMayer-Schönberger, Viktor. Big data: Rewolucja, która zmieni nasze myślenie, pracę i życie. Warszawa: MT Biznes, 2014.
Buscar texto completoSin, Tong-hŭi. Pik teit'ŏllŏji. Sŏul-si: K'ŏmyunik'eisyŏn Puksŭ, 2015.
Buscar texto completoMayer-Schönberger, Viktor. Dữ liệu lớn: Cuộc cách mạng sẽ làm thay đổi cách chúng ta sống, làm việc và tư duy. TP. Hồ Chí Minh: Nhà xuất bản Trẻ, 2014.
Buscar texto completoMayer-Schönberger, Viktor. Big data: A revolution that will transform how we live, work, and think. Boston: Mariner Books, Houghton Mifflin Harcourt, 2014.
Buscar texto completoauthor, Cukier Kenneth, Sheng Yangyan translator y Zhou Tao translator, eds. Da shu ju shi dai. Hangzhou: Zhejiang ren min chu ban she, 2013.
Buscar texto completoFrom sociology to computing in social networks: Theory, foundations and applications. Wien: Springer, 2010.
Buscar texto completoCapítulos de libros sobre el tema "Data mining – social aspects"
Hochreiter, Ronald y Christoph Waldhauser. "Data Mining Cultural Aspects of Social Media Marketing". En Advances in Data Mining. Applications and Theoretical Aspects, 130–43. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08976-8_10.
Texto completoTung, Kuan-Chieh, En Tzu Wang y Arbee L. P. Chen. "Mining Event Sequences from Social Media for Election Prediction". En Advances in Data Mining. Applications and Theoretical Aspects, 266–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_20.
Texto completoJin, Fang, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen y Naren Ramakrishnan. "Tracking Multiple Social Media for Stock Market Event Prediction". En Advances in Data Mining. Applications and Theoretical Aspects, 16–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62701-4_2.
Texto completoNohuddin, Puteri N. E., Rob Christley, Frans Coenen y Christian Setzkorn. "Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database". En Advances in Data Mining. Applications and Theoretical Aspects, 464–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14400-4_36.
Texto completoAl Essa, Ali y Miad Faezipour. "MapReduce and Spark-Based Analytic Framework Using Social Media Data for Earlier Flu Outbreak Detection". En Advances in Data Mining. Applications and Theoretical Aspects, 246–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62701-4_19.
Texto completoHuang, Chun-Che, Yu-Jie Fang, Shian-Hua Lin, Wen-Yau Liang y Shu-Rong Wu. "Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon". En Advances in Data Mining. Applications and Theoretical Aspects, 139–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_11.
Texto completoBorms, Samuel, Kris Boudt, Frederiek Van Holle y Joeri Willems. "Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies". En Data Science for Economics and Finance, 217–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_10.
Texto completoAggarwal, Charu C. "Social Network Analysis". En Data Mining, 619–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_19.
Texto completoKapoor, Komal y Jaideep Srivastava. "Data Mining". En Encyclopedia of Social Network Analysis and Mining, 332–41. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_56.
Texto completoMarcus, Sherry E., Melanie Moy y Thayne Coffman. "Social Network Analysis". En Mining Graph Data, 443–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch17.
Texto completoActas de conferencias sobre el tema "Data mining – social aspects"
Gino, Henrique L. S., Diogenes S. Pedro, Jean R. Ponciano, Claudio D. G. Linhares y Agma J. M. Traina. "Exploratory Analysis on Market Basket Data using Network Visualization". En Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.229505.
Texto completoChang, Chia-Hui y Kun-Chang Tsai. "Aspect Summarization from Blogsphere for Social Study". En 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.42.
Texto completo"E-Government Development Models: Review of Social-Technical Security Aspect". En International conference on Intelligent Systems, Data Mining and Information Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0414057.
Texto completoSouza, Bruno Á., Alice A. F. Menezes, Carlos M. S. Figueiredo, Fabíola G. Nakamura y Eduardo F. Nakamura. "Detecção de Categorias de Aspectos Utilizando Redes Neurais Profundas em Avaliações Online". En VII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/brasnam.2018.3582.
Texto completoKim, Seungbae, Jyun-Yu Jiang y Wei Wang. "Discovering Undisclosed Paid Partnership on Social Media via Aspect-Attentive Sponsored Post Learning". En WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441803.
Texto completoVukašinović, Sandra. "Demographic aspects of the planned resettlement of settlements in Lazarevac municipality". En Population in Post-Yugoslav Countries: (Dis)Similarities and Perspectives. Institute of Social Sciences, 2024. http://dx.doi.org/10.59954/ppycdsp2024.50.
Texto completoInácio, Andrei De Souza, Leandro Takeshi Hatori, Matheus Gutoski, André Eugênio Lazzaretti y Heitor Silvério Lopes. "Análise da fragmentação partidária na Assembleia Legislativa do Rio Grande do Sul com Métodos de Mineração de Dados". En VIII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2019.6558.
Texto completoDuluta, Andreistefan, Stefan Mocanu, Daniela Saru, Radu nicolae Pietraru y Mihai Craciunescu. "MODERN TECHNIQUES ON LEARNING STRATEGIES SUPPORTED BY DATA MINING ANALYSIS". En eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-100.
Texto completoMorina, Gazmend y Gani Kastrati. "ENVIRONMENTAL EXPENDITURE OF ENTERPRISES, IN MINING SECTOR IN KOSOVO". En 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/5.1/s21.072.
Texto completoBustamante, Juan, Leonardo Kuffo, Edgar Izquierdo y Carmen Vaca. "Automated Detection of Customer Experience through Social Platforms". En CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8347.
Texto completoInformes sobre el tema "Data mining – social aspects"
Fairchild, Geoffrey, Rian Mustafa Bahran y Garrett Earl McMath. Working Group on Social Internet Data Mining and Analytics for Nuclear Nonproliferation. Office of Scientific and Technical Information (OSTI), octubre de 2015. http://dx.doi.org/10.2172/1223758.
Texto completoPriester, Michael, Malaika Masson y Martin Walter. Incentivizing Clean Technology in the Mining Sector in Latin America and the Caribbean: The Role of Public Mining Institutions. Inter-American Development Bank, diciembre de 2013. http://dx.doi.org/10.18235/0009148.
Texto completoRipoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane y Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), enero de 2021. http://dx.doi.org/10.19088/sshap.2021.001.
Texto completoRipoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane y Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), enero de 2021. http://dx.doi.org/10.19088/sshap.2021.001.
Texto completoSchmidt-Sane, Megan, Tabitha Hrynick, Jennifer Cole, Santiago Ripoll y Olivia Tulloch. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), enero de 2021. http://dx.doi.org/10.19088/sshap.2021.009.
Texto completoOppel, Annalena. Beyond Informal Social Protection – Personal Networks of Economic Support in Namibia. Institute of Development Studies (IDS), noviembre de 2020. http://dx.doi.org/10.19088/ids.2020.002.
Texto completoLora, Eduardo. The Distance between Perception and Reality in the Social Domains of Life. Inter-American Development Bank, agosto de 2013. http://dx.doi.org/10.18235/0011489.
Texto completoMayne, Alison, Christina Noble, Paula Duffy, Kirsten Gow, Alexander Glasgow, Kevin O’Neill, Jeni Reid y Diana Valero. Navigating Digital Ethics for Rural Research: Guidelines and recommendations for researchers and administrators of social media groups. DigiEthics: Navigating Digital Ethics for Rural Research, noviembre de 2023. http://dx.doi.org/10.57064/2164/22326.
Texto completoDeJaeghere, Joan, Vu Dao, Bich-Hang Duong y Phuong Luong. Inequalities in Learning in Vietnam: Teachers’ Beliefs About and Classroom Practices for Ethnic Minorities. Research on Improving Systems of Education (RISE), febrero de 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/061.
Texto completoWickenden, Mary. Practical Guides for Participatory methods: Disability Inclusive Research. Institute of Development Studies, agosto de 2023. http://dx.doi.org/10.19088/ids.2023.045.
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