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 <channel rdf:about="http://ebiquity.umbc.edu/tag/datamining/">
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/269/Managing-the-Assured-Information-Sharing-Lifecycle"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/312/Privacy-Preserving-Distributed-Data-Mining-A-Multi-objective-Optimization-and-Algorithmic-Game-theoretic-Approach"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
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 <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>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/269/Managing-the-Assured-Information-Sharing-Lifecycle">
  <title><![CDATA[Managing the Assured Information Sharing Lifecycle]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/269/Managing-the-Assured-Information-Sharing-Lifecycle</link>
  <description><![CDATA[We live in the information age, a time when data and knowledge is plentiful and easily moved, processed and mined by machines. This has made it easier to discover knowledge and more efficiently manage our affairs but has raised concerns about information security, confidentiality, privacy and trust.  Balancing these is particularly urgent today in organizations responsible for national defense, law enforcement, health care, emergency services and finance.  The 9/11 Commission addressed this i...]]></description>
  <dc:date>2009-06-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/312/Privacy-Preserving-Distributed-Data-Mining-A-Multi-objective-Optimization-and-Algorithmic-Game-theoretic-Approach">
  <title><![CDATA[Privacy Preserving Distributed Data Mining: A Multi-objective Optimization and Algorithmic Game-theoretic Approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/312/Privacy-Preserving-Distributed-Data-Mining-A-Multi-objective-Optimization-and-Algorithmic-Game-theoretic-Approach</link>
  <description><![CDATA[Use of technology for data collection and analysis has seen an unprecedented growth in the last couple of decades. Individuals and organizations generate huge amount of data through everyday activities. This data is either centralized for pattern identification or mined in a distributed fashion for efficient knowledge discovery and collaborative computation. This, obviously, has raised serious concerns about privacy issues. The data mining community has responded to this challenge by deve...]]></description>
  <dc:date>2009-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks">
  <title><![CDATA[Probabilistic Approximate Algorithms for Distributed Data Mining in Peer-to-Peer Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks</link>
  <description><![CDATA[Peer-to-peer(P2P) computing is emerging as a new distributed computing 
paradigm for novel applications that involves exchange of information 
among  peers with little centralized coordination. Analyzing data 
distributed in P2P networks requires peer-to-peer data mining algorithms 
that can mine the data without data centralization. However, 
replicating  result of centralized data mining in an exact fashion is 
often communication-wise expensive. Approximate algorithms can be a 
real...]]></description>
  <dc:date>2008-04-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining">
  <title><![CDATA[Research Challenges In Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining</link>
  <description><![CDATA[Research in data mining has led to advanced knowledge discovery
technologies and applications. In this talk, we will discuss some
emerging research issues for advanced technologies and
applications in data mining and discuss some recent progress in
this direction, including (1) exploration of the power of pattern
mining, (2) analysis of multidimensional, heterogeneous and
evolving information network, (3) mining of fast changing data
streams, (4) mining of moving object data, RFID data...]]></description>
  <dc:date>2008-04-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure">
  <title><![CDATA[Empowering Scientific Discovery by Distributed Data Mining on the Grid Infrastructure]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure</link>
  <description><![CDATA[The grid-based computing paradigm has attracted much attention in recent years. The sharing of distributed computing resources (such as software, hardware, data, sensors, etc) is an important aspect of grid computing. Computational Grids focus on methods for handling compute intensive tasks while Data Grids are geared toward data-intensive computing. Grid-based computing has been put to use in several scientific disciplines such as astronomy, engineering, climate studies, ecology, biology and...]]></description>
  <dc:date>2007-09-28</dc:date>
 </item>
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