Advances in Mining Graphs, Trees and Sequences: Volume 124 Frontiers in Artificial Intelligence and Applications

Advances in Mining Graphs, Trees and Sequences: Volume 124 Frontiers in Artificial Intelligence and Applications

by L. De Raedt
     
 

ISBN-10: 1586035282

ISBN-13: 9781586035280

Pub. Date: 01/01/2006

Publisher: I O S Press, Incorporated

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

Overview

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.

Product Details

ISBN-13:
9781586035280
Publisher:
I O S Press, Incorporated
Publication date:
01/01/2006
Series:
Frontiers in Artificial Intelligence and Applications Series
Pages:
220
Product dimensions:
7.44(w) x 9.69(h) x 0.46(d)

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