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Mining of Data with Complex Structures

  • Fedja Hadzic
  • Henry Tan
  • Tharam S. Dillon

Part of the Studies in Computational Intelligence book series (SCI, volume 333)

Table of contents

  1. Front Matter
  2. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 1-21
  3. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 23-40
  4. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 41-65
  5. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 67-86
  6. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 87-138
  7. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 139-174
  8. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 175-190
  9. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 191-199
  10. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 201-247
  11. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 249-286
  12. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 287-300
  13. Fedja Hadzic, Henry Tan, Tharam S. Dillon
    Pages 301-326

About this book

Introduction

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.

Keywords

Complex Structures Computational Intelligence Data Mining Extension Racter bioinformatics knowledge

Authors and affiliations

  • Fedja Hadzic
    • 1
  • Henry Tan
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and Business Intelligence InstituteCurtin UniversityWestern AustraliaAustralia
  2. 2. Bothel WashingtonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-17557-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-17556-5
  • Online ISBN 978-3-642-17557-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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