|Conference: MLG'07 - The 5th International Workshop on Mining and Learning with Graphs |
Organized by: Università degli Studi di Firenze
Description: 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.
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