Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
An Introduction Read chapter Read first chapter

2018 | OriginalPaper | Chapter

5. Dataset Transformations and Auto-CM

Authors : Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi

Published in: Artificial Adaptive Systems Using Auto Contractive Maps

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We have looked at how to visualize the relationships among the elements of a dataset in Chap. 4. This chapter is devoted to the use of Auto-CM in the transformation of datasets for the purpose of extracting further relationships among data elements. The first transformation we call the FS-Transform, which looks beyond an all or nothing, binary relationship that is typical of most ANNs. The extraction of these perhaps more subtle relationships can be thought of as gradual relationships, zero denoting no relationship is present and one denoting a full/complete relationship that is absolutely present. It is thus, akin to a fuzzy set. The second transformation is one, which “morph” the delineation between records and variables within records that we call Hyper-Composition.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Visualization of Auto-CM Output
next chapter Advances, the K-Contractive Map: A Supervised Version of Auto-CM
Literature
1.
Buscema, M., V. Consonni, D. Ballabio, A. Mauri, G. Massini, M. Breda, and R. Todeschini. 2014. K-CM: A New Artificial Neural Network. Application to Supervised Pattern Recognition. Chemometrics and Intelligent Laboratory Systems 138: 110–119.CrossRef
2.
McCLelland, J.L., D.E. Rumelhart, and G.E. Hinton. 1986. The Appeal of Parallel Distributed Processing. In Parallel Distributed Processing, Chap. 1 vol. 1, ed. J.L. McClelland and D.E. Rumelhart, 3–44. Exploration in Microstructure of Cognition, Foundations, MIT Press.
3.
Buscema, M. 2007. A Novel Adapting Mapping Method for Emergent Properties Discovery in Data Bases: Experience in Medical Field. In 2007 IEEE International Conference on Systems, Man and Cybernetics (SMC 2007). Montreal, Canada, October 7–10, 2007.
4.
Buscema, M., and E. Grossi. 2008. The Semantic Connectivity Map: An Adapting Self-Organizing Knowledge Discovery Method in Data Bases. Experience in Gastro-oesophageal reflux disease. International Journal Data Mining and Bioinformatics 2 (4) 2008.
5.
Buscema, M., F. Newman, and E. Grossi, W. Tastle. 2010. Application of Adaptive Systems Methodology to Radiotherapy. In NAFIPS, July 12–14, 2010 Toronto, Canada.
6.
Di Ludovico, A., and G. Pieri. 2011. Artificial Neural Networks and Ancient Artefacts: Justifications for a Multiform Integrated Approach Using PST and Auto-CM Models. Archeologia e Calcolatori 22: 99–128.
7.
Coppedè, F., E. Grossi, M. Buscema, and L. Migliore. 2013. Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals. PLOS ONE, 8 (8): e74012, 1–11.
8.
Gironi, M., B. Borgiani, E. Farina, E. Mariani, C. Cursano, M. Alberoni, R. Nemni, G. Comi, M. Buscema, R. Furlan, and Enzo Grossi. 2015. A Global Immune Deficit in Alzheimer’s Disease and Mild Cognitive Impairment Disclosed by a Novel Data Mining Process. Journal of Alzheimer’s Disease 43: 1199–1213.
9.
10.
Coppedè, F., E. Grossi, A. Lopomo, and R. Spisni. 2015. M Buscema & Lucia Migliore. Application of Artificial Neural Networks to Link Genetic and Environmental Factors to DNA Methylation in Colorectal Cancer, Epigenomics 7 (2): 175–186.
11.
Buscema, M., E. Grossi, L. Montanini, and M. E. Street. 2015. Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles, PLoS ONE 10(7): June 9, 2015 e0126020. https://​doi.​org/​10.​1371/​journal.
12.
McClelland, J.L., and D.E. Rumelhart. 1988. Explorations in Parallel Distributed Processing, Chap. 2, 11–47. MIT Press.
13.
Tribal Analysis Center. 2009. Ishaqzai Tribe, Tribal Analysis Center, 6610-M Mooretown Road, Box 159. Williamsburg, VA, 23188
14.
Thomas, Ruttig. 2009. The Other Side: Dimensions of the Afghan Insurgency: Causes, Actors and Approaches to Talks, Kabul/Berlin: Afghanistan Analysts Network, Thematic Report 01/2009, July 2009.
15.
Schubert, Janis L., W. Thomas Moore, Patrick D. Finley, Ryan Hammer, and Robert J. Glass. 2010. Modeling Tribal Leadership Dynamics: An Opinion Dynamics Model of Pashtun Leadership Selection. Sandia National Laboratories.
16.
Buscema, M., and P. L. Sacco. 2010. Auto-contractive Maps, The H Function, and The Maximally Regular Graph (MRG): A New Methodology for Data Mining. In Applications of Mathematics in Models, Artificial Neural Networks and Arts, Chap. 11, eds.V. Capecchi et al. https://​doi.​org/​10.​1007/​978-90-481-8581-8_​11. Springer.
17.
Buscema, M., M. Asadi-Zeydabadi, W. Lodwick, and M. Breda. 2016. The H0 Function, A New Index for Detecting Structural/Topological Complexity Information in Undirected Graphs. Physica A 447: 355–378.MathSciNetCrossRef
Metadata
Title
Dataset Transformations and Auto-CM
Authors
Paolo Massimo Buscema
Giulia Massini
Marco Breda
Weldon A. Lodwick
Francis Newman
Masoud Asadi-Zeydabadi
Copyright Year
2018
Book
Artificial Adaptive Systems Using Auto Contractive Maps

Print ISBN: 978-3-319-75048-4

Electronic ISBN: 978-3-319-75049-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75049-1_5

Premium Partner

    Image Credits