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Relevant bibliographies by topics / Data mining – social aspects

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Dissertations / Theses

Academic literature on the topic 'Data mining – social aspects'

Author: Grafiati

Published: 26 July 2024

Last updated: 26 July 2024

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Dissertations / Theses on the topic "Data mining – social aspects"

1

Chen, Weidong. "Discovering communities by information diffusion and link density propagation." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1422.

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2

Nguyen, 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.

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Analyzing a huge amount of data becomes a tricky challenge and an opportunity for data miners and businessmen today. Knowledge management processes can deal with big knowledge source to find tacit intelligence making businesses more agile and effective. Data mining is a powerful tool working with big data to create capabilities of forecasting and analysis. Yet there is a lack of research on where and how data mining can add value in knowledge management processes in organizations to maximize valuable knowledge for innovation and business management. The knowledge management processes of a psyc

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3

Yang, Shuang-Hong. "Predictive models for online human activities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.

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The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of online human activities is not only fundamental to the understanding of human behavior, but also important to the online industry. This thesis focuses on developing models and algorithms to predict human activities in online systems and to improve the algorithmic design of personalized/socialized systems (e.g., recommendation, advertising, Web search systems). We are particularly interested in three types of online user

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4

Cai, Zhongming. "Technical aspects of data mining." Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.

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5

Eriksson, Jesper, and 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.

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Businesses today are often operating in a highly competitive environment where information is a noticeably valuable asset. Businesses are therefore in need of powerful tools for extracting actionable business knowledge. Research show that SME companies are lagging behind large companies in the use of data analytics; even though they know the potential benefits. We want to study and compare different tools for data analytics and how they can be used by small companies. Our research questions are therefore: what analytical tools are today available on the market, and what are their possibilities

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6

Wang, Guan. "Graph-Based Approach on Social Data Mining." Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.

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<p> Powered 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. </p><p> Motivated by this trend, my research addres

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7

Ip, Lai Cheng. "Mining on social network community for marketing." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.

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8

Costa, 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/.

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Social media services have a growing impact in our society. Individuals often rely on social media to get their news, decide which products to buy or to communicate with their friends. As consequence of the widespread adoption of social media, a large volume of data on how users behave is created every day and stored into large databases. Learning how to analyze and extract useful knowledge from this data has a number of potential applications. For instance, a deeper understanding on how legitimate users interact with social media services could be explored to design more accurate spam and fra

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9

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/.

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Social Networking Sites (SNS) are ubiquitous within modern society, forming communications networks that span across cultural and geographical boundaries. The information posted to these sites provide useful insights into individuals, but can also provide a wealth of information that can be used for further analysis into the surrounding environment. Three main challenges limit the use of this information in applications: the quantity of data is often unmanageable, there is a significant amount of data unavailable for use due to a lack of generic interfaces for access, and there is difficulty i

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10

Alsaleh, 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.

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This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

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