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Advances in mining graphs, trees and sequences. Papers based on the presentation at the 1st international workshop on mining graphs, trees and sequences, (MGTS-2003), Cavtat-Dubrovnik, Croatia, September 23, 2003. (English) Zbl 1084.68096

Frontiers in Artificial Intelligence and Applications 124. Amsterdam: IOS Press (ISBN 1-58603-528-2/hbk). x, 209 p. (2005).
Publisher’s description: Ever since the early days of machine learning and data mining, it has been realized that the traditional attribute-value and item-set representations are too limited for many practical applications in domains such as chemistry, biology, network analysis and text mining. This has triggered a lot of research on mining and learning within alternative and more expressive representation formalisms such as computational logic, relational algebra, graphs, trees and sequences. The motivation for using graphs, trees and sequences. Is that they are 1) more expressive than flat representations, and 2) potentially more efficient than multi-relational learning and mining techniques. At the same time, the data structures of graphs, trees and sequences 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 and machine learning that are both theoretically well-founded and widely applicable. The goal of this book is to collect recent outstanding studies on mining and learning within graphs, trees and sequences in studies worldwide.
The articles of this volume will not be reviewed individuall

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68R10 Graph theory (including graph drawing) in computer science
68-06 Proceedings, conferences, collections, etc. pertaining to computer science
00B25 Proceedings of conferences of miscellaneous specific interest
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