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[Home] [Call For Papers]
[Accepted Papers] [Workshop Program] [Program Committee]
As data collection sources and
channels continuous evolve, mining and correlating information from multiple information
sources has become a crucial step in data mining and knowledge discovery.
Several emerging fields and applications from healthcare informatics to
environmental sciences to social computing in particular call for data mining
methodologies and approaches to deal with multi-source data. On one hand,
comparing patterns from different databases and understanding their
relationships can be extremely beneficial for these applications. On the other
hand, many data mining and data analysis tasks such as classification,
regression, and clustering, can significantly improve their performance if
information from different sources can be properly integrated and leveraged.
The aim of this workshop is to bring together
data mining experts to advance research on integrating and mining multiple
information sources, identify key research issues, and discuss the latest
results on this new frontier of data mining. Representative issues to be
addressed include but are not limited to
Machine Learning in Multi-source Environments
· Multi-view learning, multi-task learning, transfer learning · Ensemble learning and ensemble clustering Multiple information source data mining applications and case studies· Data mining applications with multi-source data confirmation and learning platform
· Case studies on multi-source data mining for industrial applications
Information Integration and Harnessing Complex Data Relationship · Database similarity assessment · Automatic schema mapping and relationship discovery · New mapping framework for multiple information sources · Data source classification and clustering · Data cleansing, data preparation, data/pattern selection, conflict and inconsistency resolution Integrative and Cooperative Mining · Model integration for heterogeneous information sources · Mode transferring across different data domains · Incremental and scalable data mining algorithms Differentiation and Correlation · Local pattern analysis and fusion · Global pattern synthesizing and assessment · Merging local rules for global pattern discovery · Pattern summarization from multiple datasets · Multi-dimensional pattern search and comparison · Pattern comparison across multiple data sources · Inter pattern discovery from complex data sources Stream data mining algorithms · Clustering and classification of data of changing distributions · Data stream processing, storage, and retrieval systems · Sensor networking Interactive data mining systems · Query languages for mining multiple information sources · Query optimization for distributed data mining · Distributed data mining operators in supporting interactive data mining queries
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Paper Types |
We
solicit research papers with maximum
8 pages for all submissions inclusive of all references and figures.
The
submission should focus on new designs, algorithms, and solutions for mining
multiple information sources. All papers should be submitted in IEEE
proceedings format (two columns). Please follow the IEEE Computer Society Press Proceedings Author
Guidelines at http://www.computer.org/portal/pages/cscps/cps/final/icdm06.xml
The
workshop proceedings will be published by the IEEE Digital Library and
distributed during the workshop
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Paper Submission |
For submission of the paper, please use
submission system Here
If you are experiencing any difficulties, please
contact workshop co-chairs.
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Special Issue Publication |
Selected workshop papers will be invited for journal publication (subject
to additional review) in the Neurocomputing,
Special Issue on Data Mining Applications and Case Study (approved). The
journal submission due date is around late October 2010.
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Important Dates |
- August
13, 2010: Extended
submission due date
- Sept. 20,
2010: Author
notification
- Oct. 11, 2010: Submission
of Camera-ready papers
- Dec. 13,
2010: Workshop in