PUMA publications for /user/stephandoerfel/frequenthttps://puma.uni-kassel.de/user/stephandoerfel/frequentPUMA RSS feed for /user/stephandoerfel/frequent2024-03-28T18:29:00+01:00Frequent Pattern Mining in Web Log Datahttps://puma.uni-kassel.de/bibtex/2f29f4627c9ae99370fc7ba005982e2e6/stephandoerfelstephandoerfel2013-03-30T17:32:37+01:00association frequent itemset mining pattern rule weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Renáta Iváncsy" itemprop="url" href="/author/Ren%c3%a1ta%20Iv%c3%a1ncsy"><span itemprop="name">R. Iváncsy</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="István Vajk" itemprop="url" href="/author/Istv%c3%a1n%20Vajk"><span itemprop="name">I. Vajk</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Acta Polytechnica Hungarica</em></span></span> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Sat Mar 30 17:32:37 CET 2013Acta Polytechnica Hungarica1Frequent Pattern Mining in Web Log Data32006association frequent itemset mining pattern rule weblog Abstract: Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs.CiteSeerX — Frequent Pattern Mining in Web Log Data