Advances in Mining Graphs, Trees and Sequences: v. 124 (Frontiers in Artificial Intelligence and Applications) [Paperback] Washio, Takashi; Kok, Joost N. and Raedt, Luc De

ISBN 10: 1586035282 / ISBN 13: 9781586035280

Verlag: SAGE Publications Ltd, 2006

Beschreibung

Unread, as new.

Bestandsnummer des Verkäufers 037544-1

Bibliografische Details

• Titel: Advances in Mining Graphs, Trees and Sequences: v. 124 (Frontiers in Artificial Intelligence and Applications) [Paperback] Washio, Takashi; Kok, Joost N. and Raedt, Luc De
• Verlag: SAGE Publications Ltd
• Erscheinungsjahr: 2006
• Sprache: Englisch
• ISBN-10: 1586035282
• ISBN-13: 9781586035280
• Einband: Softcover
• Zustand: Fine

Über diesen Titel

Inhaltsangabe

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.

Aus dem Klappentext

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. This book collects recent outstanding studies on mining and learning within graphs, trees and sequences in studies worldwide.