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  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=fuzzy+clustering]]></link>
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      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/153/Intrusion-Detection-Modeling-System-State-to-Detect-and-Classify-Aberrant-Behavior"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/95/On-Creating-Adaptive-Web-Servers-Using-Weblog-Mining"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs"/>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization">
  <title><![CDATA[Web/Data Mining and Personalization]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization</link>
  <description><![CDATA[The evolution of the Internet into the Global Information Infrastructure, coupled with the immense
    popularity of the Web, has also enabled the ordinary citizen to become not just a consumer of information, but also its
    disseminator. The Web, then, is becoming the apocryphal Vox Populi. Given that there is this vast and ever growing
    amount of information, how does the average user quickly find what s/he is looking for -- a task in which the present
    day search engines don'...]]></description>
  <dc:date>1999-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/153/Intrusion-Detection-Modeling-System-State-to-Detect-and-Classify-Aberrant-Behavior">
  <title><![CDATA[Intrusion Detection:  Modeling System State to Detect and Classify Aberrant Behavior]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/153/Intrusion-Detection-Modeling-System-State-to-Detect-and-Classify-Aberrant-Behavior</link>
  <description><![CDATA[We present a dual-phase host-based intrusion detection process. We have demonstrated, through experimental validation, that our process improves the current state of intrusion detection
capabilities. The first phase uses cluster analysis to compare samples of low-level
operating system data to an established model of normalcy. The second phase takes instances
of non-conforming data from phase-1, maps that data to instances of our target-centric ontology
and reasons over it. The reasoning ...]]></description>
  <dc:date>2004-02-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection">
  <title><![CDATA[Fuzzy Clustering for Intrusion Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection</link>
  <description><![CDATA[The newly formed Department of Homeland Security has been mandated to reduce America's vulnerability to terrorism. In addition to being charged with physical protection, this newly formed department is also responsible for protecting the nation's critical infrastructure. Protecting computer systems from intrusions is an important aspect of securing the nation's infrastructure. We are exploring how fuzzy data mining and concepts introduced by the semantic Web can operate in synergy to perform ...]]></description>
  <dc:date>2003-04-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/95/On-Creating-Adaptive-Web-Servers-Using-Weblog-Mining">
  <title><![CDATA[On Creating Adaptive Web Servers Using Weblog Mining]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/95/On-Creating-Adaptive-Web-Servers-Using-Weblog-Mining</link>
  <description><![CDATA[Personalization of content returned from a web site is an important problem in general, and
affects e-commerce and e-services in particular. Targeting appropriate information or products
to the end user can significantly change (for the better) the users experience on a web site. One
possible approach to web personalization is to mine typical user profiles from the vast amount
of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised
classification ...]]></description>
  <dc:date>2000-11-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs">
  <title><![CDATA[On Mining Web Access Logs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs</link>
  <description><![CDATA[The proliferation of information on the world wide web has made
the personalization of this information space a necessity. One
possible approach to web personalization is to mine typical user
profiles from the vast amount of historical data stored in access
logs. In the absence of any a priori knowledge, unsupervised
classification or clustering methods seem to be ideally suited to
analyze the semi-structured log data of user accesses. In this paper,
we define the notion of a “user s...]]></description>
  <dc:date>2000-05-14</dc:date>
 </item>
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