Search for dissertations about: "Web mining"
Showing result 1 - 5 of 19 swedish dissertations containing the words Web mining.
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1. Everyday mining : Exploring sequences in event-based data
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
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2. Mining Web Logs to Improve User Experience in Web Search
Abstract : The World Wide Web continues to grow in size and diversity and this makes it increasinglyhard for users to find valuable information because of heterogeneous form and contentof the documents, little knowledge about the reliability and prestige of the documents anda great deal of redundancy.Usually search engines look for documents that contain specific keywords or phrasesstated by the users as queries. READ MORE
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3. Finding, extracting and exploiting structure in text and hypertext
Abstract : Data mining is a fast-developing field of study, using computations to either predict or describe large amounts of data. The increase in data produced each year goes hand in hand with this, requiring algorithms that are more and more efficient in order to find interesting information within a given time. READ MORE
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4. Development and Evaluation of Web Applications for Investigating Candidate Genes in Rat Models of Complex Diseases
Abstract : Many human diseases, such as rheumatoid arthritis and type 2 diabetes mellitus, have a very complex development, depending on both environmental and multiple genetic factors. By crossing inbred rat strains susceptible to a genetic disorder with strains resistant to the same disorder, genomic regions associated with the disease can be identified, so called quantitative trait loci (QTLs). READ MORE
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5. Learning predictive models from graph data using pattern mining
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