Overview
- The state of the art of Mining of Data with Complex Structures
- Clarifies the type and nature of data with complex structure including sequences, trees and graphs
- Written by leading experts in this field
Part of the book series: Studies in Computational Intelligence (SCI, volume 333)
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Table of contents (12 chapters)
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Front Matter
Keywords
About this book
Mining of Data with Complex Structures:
- Clarifies the type and nature of data with complex structure including sequences, trees and graphs
- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.
- Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.
- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)
- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.
- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.
- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.
- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.
- Details the extension of the TMG framework for sequence mining
- Provides an overview of the future research direction with respect to technical extensions and application areas
The primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry.In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.
Authors and Affiliations
Bibliographic Information
Book Title: Mining of Data with Complex Structures
Authors: Fedja Hadzic, Henry Tan, Tharam S. Dillon
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-17557-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-17556-5Published: 30 January 2011
Softcover ISBN: 978-3-642-26703-1Published: 25 February 2013
eBook ISBN: 978-3-642-17557-2Published: 03 February 2011
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XX, 328
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Mathematical and Computational Engineering