MMIS 2011 - The 5th International Workshop on Mining Multiple Information Sources

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.
Topics of Interest
Representative issues to be addressed include but are not limited to:
1. Transfer learning from multiple information sources
2. Pattern correlation and differentiation in different data sources
3. Integrative and cooperative mining
4. Data integration and harnessing complex data relationship
5. Multi-source data mining applications and case studies