<P><SPAN style=″FONT–FAMILY: Arial″><SPAN style=″FONT–SIZE: 10pt; FONT–FAMILY: ′Tahoma′″><SPAN style=″FONT–FAMILY: Arial″><SPAN style=″FONT–SIZE: 10pt; FONT–FAMILY: ′Tahoma′″>The 2008 edition of the <STRONG><SPAN class=caps>IEEE</SPAN> International Conference on Data Mining</STRONG> series (<SPAN class=caps>ICDM</SPAN> 2008) will be held in Pisa, Italy, on <STRONG>December 15 thru 19, 2008</STRONG>.</P> <P>The International Conference on Data Mining series (<SPAN class=caps>ICDM</SPAN>) is well established as a top ranked research conference in data mining, providing a premier forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences.</P></SPAN><?xml:namespace prefix = o ns = ″urn:schemas–microsoft–com:office:office″ /></SPAN></SPAN></SPAN> <P><B>Keywords:</B> </P> <H3>Data mining foundations</H3> <UL> <LI>Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, pattern discovery, and association analysis) <LI>Models and algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains <LI>Developing a unifying theory of data mining <LI>Mining sequences and sequential data <LI>Mining spatial and temporal datasets <LI>Mining textual and unstructured datasets <LI>Distributed data mining <LI>High performance implementations of data mining algorithms <LI>Privacy– and anonymity–preserving data analysis </LI></UL> <H3>Mining in emerging domains</H3> <UL> <LI>Stream Data Mining <LI>Mining moving object data, <SPAN class=caps>RFID</SPAN> data, and data from sensor networks <LI>Ubiquitous knowledge discovery <LI>Mining multi–agent data <LI>Mining and link analysis in networked settings: web, social and computer networks, and online communities <LI>Mining the semantic web <LI>Data mining in electronic commerce, such as recommendation, sponsored <LI>web search, advertising, and marketing tasks </LI></UL> <H3>Methodological aspects and the <SPAN class=caps>KDD</SPAN> process</H3> <UL> <LI>Data pre–processing, data reduction, feature selection, and feature transformation <LI>Quality assessment, interestingness analysis, and post–processing <LI>Statistical foundations for robust and scalable data mining <LI>Handling imbalanced data <LI>Automating the mining process and other process related issues <LI>Dealing with cost sensitive data and loss models <LI>Human–machine interaction and visual data mining <LI>Integration of data warehousing, <SPAN class=caps>OLAP</SPAN> and data mining <LI>Data mining query languages <LI>Security and data integrity </LI></UL> <H3>Integrated <SPAN class=caps>KDD</SPAN> applications, systems, and experiences</H3> <UL> <LI>Bioinformatics, computational chemistry, ecoinformatics <LI>Computational finance, online trading, and analysis of markets <LI>Intrusion detection, fraud prevention, and surveillance <LI>Healthcare, epidemic modeling, and clinical research <LI>Customer relationship management <LI>Telecommunications, network and systems management <LI>Sustainable mobility and intelligent transportation systems </LI></UL>
Abbrevation
ICDM
City
Pisa
Country
Italy
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End Date
Abstract