Introduction
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
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Important
Dates
August
5
August 15, 2011: Due
date for full papers
September 20, 2011: Notification
of acceptance
October 14, 2011: Camera-ready
of accepted papers
December 11, 2011: Workshop date
Paper
Submission
A maximum of 8 pages in ICDM-11 format.
The paper submission site for MMIS-11 is here.
Workshop
Co-Organizers
Bin Li
University of Technology, Sydney
(UTS), Australia
Xingquan Zhu
University of Technology, Sydney
(UTS), Australia
Qiang Yang
Hong Kong University of Science
& Technology, Hong Kong
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