Search for dissertations about: "data mining"

Showing result 1 - 5 of 290 swedish dissertations containing the words data mining.

  1. 1. Everyday mining Exploring sequences in event-based data

    University dissertation from Norrköping : Linköping University Electronic Press

    Author : Katerina Vrotsou; Matthew Cooper; Anders Ynnerman; Kajsa Ellegård; Andrew S. Harvey; [2010]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Event-based data; activity diary data; event-sequences; interactive exploration; sequence identification; visual data mining; TECHNOLOGY Information technology Computer science; TEKNIKVETENSKAP Informationsteknik Datavetenskap;

    Abstract : Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. READ MORE

  2. 2. Legal Implications of Data Mining : Assessing the European Union’s Data Protection Principles in Light of the United States Government’s National Intelligence Data Mining Practices

    University dissertation from Visby : Ragulka förlag

    Author : Liane Colonna; Stockholms universitet.; [2016]
    Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; data mining; data protection; privacy; Directive 95 46 ec; data transfers; EU law; US law; rättsinformatik; Law and Information Technology;

    Abstract : This dissertation addresses some of the data protection challenges that have arisen from globalization, technological progress, terrorism and seamless cross-border flows of personal data.  The focus of the thesis is to examine ways to protect the personal data of EU citizens, which may be collected by communications service providers such as Google and Facebook, transferred to the US Government and data mined within the context of American national intelligence surveillance programs. READ MORE

  3. 3. Data mining of geospatial data: combining visual and automatic methods

    University dissertation from Stockholm : KTH

    Author : Urška Demšar; KTH.; [2006]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; geographic information science; geoinformatics; geovisualisation; spatial data mining; visual data mining; usability evaluation; TECHNOLOGY Information technology Other information technology; TEKNIKVETENSKAP Informationsteknik Övrig informationsteknik;

    Abstract : Most of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data. READ MORE

  4. 4. Algorithmically Guided Information Visualization Explorative Approaches for High Dimensional, Mixed and Categorical Data

    University dissertation from Linköping : Linköping University Electronic Press

    Author : Sara Johansson Fernstad; Linköpings universitet.; Linköpings universitet.; [2011]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Information visualization; data mining; high dimensional data; categorical data; mixed data;

    Abstract : Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. READ MORE

  5. 5. Learning predictive models from graph data using pattern mining

    University dissertation from Stockholm : Department of Computer and Systems Sciences, Stockholm University

    Author : Thashmee M. Karunaratne; Stockholms universitet.; [2014]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Graph Data; Pattern Mining; Classification; Regression; Predictive Models; Computer and Systems Sciences; data- och systemvetenskap;

    Abstract : Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. READ MORE