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The Fifth International Workshop on

Mining Multiple Information Sources (MMIS-11)

In conjunction with The IEEE International Conference on Data Mining (ICDM-11), December 11, 2011, Vancouver, Canada

As data collection channels and means become more and diverse, many real-world data mining tasks can easily acquire multiple data sets from various information sources. Compared to single-source mining problems in which all the data for a mining task are in the same pattern representation and are assumed to be drawn from the identical distribution, a multi-source mining problem is built on multiple information sources which have different contributions to the target task and can complement one another to boost the performance. To better leverage multiple information sources, integrating and transferring knowledge among multiple data sets has become a crucial step in data mining.

We call for papers for addressing data mining problems in multi-source scenarios. On one hand, many data mining and analysis tasks can significantly improve their performance if knowledge mined from multiple sources can be properly integrated and shared. On the other hand, comparing patterns from different data sources and understanding their relatedness can be beneficial for applications ranging from social science to bioinformatics to economics. Thus, it becomes urgent to develop theories, methods, applications, and knowledge representations, for mining from multiple information sources that share relatedness.

Topics of Interest

Representative issues to be addressed include but are not limited to:

1.     Transfer learning from multiple information sources

        Transfer learning from heterogeneous and structured data sources

        Transfer learning from stream data sources

        Foundation and theories of transfer learning and domain adaptation

2.     Pattern correlation and differentiation in different data sources

        Pattern comparison across multiple data sources

        Pattern fusion and synthesizing from multiple data sources

        Pattern summarization from multiple data sources

3.     Integrative and cooperative mining

        Model integration and fusion from multiple information sources

        Ensemble learning from multiple data sources

        Multi-view learning from multiple data sources

4.     Data integration and harnessing complex data relationship

        Database similarity assessment and quantification

        Automatic schema mapping and relationship discovery

        New mapping framework for multiple information sources

5.      Multi-source data mining applications and case studies

        Web and social media mining

        Reality mining, urban and environment sensing

        Bioinformatics and biomedical data mining

Paper Submission

All papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines with a maximum of 8 pages in the 2-column format. Please visit the IEEE ICDM 2011 website for detailed formatting and submission guidelines. Papers that do not comply with the Submission Guidelines will be rejected without review. The workshop proceedings will be published by the IEEE Digital Library.