MTGS 2004 @ ECML/PKDD 2004, Pisa, Italy, September 20-24, 2004
webmaster contact at snijssen@liacs.nl

Scope of the workshop

Ever since the early days of machine learning, data mining and pattern recognition, it has been realized that the traditional attribute-value and item-set representations are too limited for many practically applications in domains such as chemistry, biology, network analysis, imaging and text mining. This has triggered research on mining and learning within more expressive representation formalisms such as computational logic, relational algebra, graphs, trees and sequences.

The state-of-the-art is that attribute-value and item-set representations lie at one extreme end of the spectrum, and multi-relational data mining and inductive logic programming at the other end. The middle is occupied by traditional data structures employed throughout the field of computer science. These include graphs, trees and sequences (or strings) and are among the best understood and most widely applied representations within computer science. Thus these representations offer ideal opportunities for developing interesting contributions in data mining, machine learning and pattern recognition that are both theoretically well-founded and widely applicable.

Topics and goals

We are looking for contributions related to graph, tree and sequence structure mining and learning. More specifically, the workshop will focus on the following topics:

  • Applications to real world problems in biology, chemistry, XML, etc.
  • Frequent structure mining algorithms, structural pattern recognition algorithms, clustering algorithms, classification algorithms, supervised and unsupervised learning algorithms, as long as the algorithms are applied to graph structures or sequences
  • The relation between topological classes and language classes
  • Efficiency issues in graph, tree and sequence mining and learning
  • Identifying interesting subclasses that can efficiently be mined or learned
  • Basic principles of graph, tree and sequence mining
  • Analysis of the complexity of graph, tree and sequence mining
  • Relationship of graph, tree and sequence mining to other techniques
  • Any other result relevant to graph, tree and sequence mining
  • Novel research linked to graph, tree and sequence mining