Data Mining and Machine Learning are in the midst of a "structured revolution". After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which examples consist of collections of inter-related entities or are linked together into complex graphs. A major driving force is the explosive growth in the amount of heterogeneous data that is being collected in the business and scientific world. Example domains include bioinformatics, chemoinformatics, transportation systems, communication networks, social network analysis, link analysis, robotics, among others. The structures encountered can be as simple as sequences and trees (such as those arising in protein secondary structure prediction and natural language parsing) or as complex as citation graphs, the World Wide Web, and even relational data bases. In all these cases, structured representations can give a more informative view of the problem at hand, which is often crucial for the development of successful mining and learning algorithms.
There have been several workshops on mining and learning from graphs in recent years such as last year's MLG and its forerunner MGTS workshop series on Mining Graphs, Trees and Sequences. These were successful, but were tied to the conference of one research community. Nowadays there seems to be a surge of interest in mining and learning from structured data across several communities. Most researchers, however, only have exposure to one or two communities, and no clear understanding of the relative advantages and limitations of different approaches has yet emerged. We believe this is an ideal time for a workshop that allows active researchers in this area to discuss and debate the unique challenges of mining and learning from structured data. The MLG 2007 workshop will thus concentrate on mining and learning with structured data in general and its many appearances and facets such as interpretations, graphs, trees, sequences. Specifically, we seek to invite researchers in Statistical Relational Learning, Kernel Methods for Structured Inputs/Outputs, Graph Mining, (Multi-) Relational Data Mining, Inductive Logic Programming, among others.
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