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  • The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor's needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors' behaviour in different manners depending on desired further processing. Web server access log files will be used as source data. It is possible to use for example the combination of IP address and user-agent string to identify a single visitor. This task can currently be solved using statistical analysis -- spec
  • The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor's needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors' behaviour in different manners depending on desired further processing. Web server access log files will be used as source data. It is possible to use for example the combination of IP address and user-agent string to identify a single visitor. This task can currently be solved using statistical analysis -- spec (en)
Title
  • Using Genetic Algorithms for Web Usage Mining
  • Using Genetic Algorithms for Web Usage Mining (en)
skos:prefLabel
  • Using Genetic Algorithms for Web Usage Mining
  • Using Genetic Algorithms for Web Usage Mining (en)
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  • RIV/62156489:43110/09:00144595!RIV10-MSM-43110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6215648904)
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  • 347981
http://linked.open...ai/riv/idVysledku
  • RIV/62156489:43110/09:00144595
http://linked.open...riv/jazykVysledku
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  • web usage mining; access patterns; genetic algorithms; web log files (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F0795AD482AE]
http://linked.open...v/mistoKonaniAkce
  • Nitra
http://linked.open...i/riv/mistoVydani
  • Univerzita Konštantína Filozofa v Nitre
http://linked.open...i/riv/nazevZdroje
  • ASIS 2009 -- Adaptívne siete v informačných systémoch
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Popelka, Ondřej
  • Šťastný, Jiří
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Nitra
https://schema.org/isbn
  • 978-80-8094-593-0
http://localhost/t...ganizacniJednotka
  • 43110
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