本文由中国论文联盟
The aim of the ICDDM conference series is to provide a forum for laying the foundations of a new principled approach to Database and Data Mining. To this end, the meeting aims to attract participants with different backgrounds, to foster cross-pollination between different research fields, and to expose and discuss innovative theories, frameworks, methodologies, tools, and applications.
The ICDDM 2010 is sponsored by The International Association of Computer Science and Information Technology (IACSIT), and supported by IACSIT Members and scholars all round the world. Submitted conference papers will be reviewed by technical committees of the Conference.
ICDDM 2010 will be published in the conference proceeding, and will be included in the IEEE Xplore and CSDL, and submitted to INSPEC, Thomson ISI Proceeding (ISTP), Ei Compendex for indexing.
News! ICDDM 2010 has been listed in IACSIT Conference Calendar.
Topics
The topics of interest to ICDDM 2010 include, but are not limited to: Data mining foundations
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis)
Algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains
Developing a unifying theory of data mining
Mining sequences and sequential data
Mining spatial and temporal datasets
Mining textual and unstructured datasets
High performance implementations of data mining algorithms
Mining in targeted application contexts
Mining high speed data streams
Mining sensor data
Distributed data mining and mining multi-agent data
Mining in networked settings: web, social and computer networks, and online communities
Data mining in electronic commerce, such as recommendation, sponsored web search, advertising, and marketing tasks
Methodological aspects and the KDD process
Data pre-processing, data reduction, feature selection, and feature transformation
Quality assessment, interestingness analysis, and post-processing
Statistical foundations for robust and scalable data mining
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