Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
An Introduction Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

7. Comparison of Auto-CM to Various Other Data Understanding Approaches

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

Erschienen in: Artificial Adaptive Systems Using Auto Contractive Maps

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We compare Auto-CM with various other methods that extract patterns from data. The way that we measure the results of comparisons uses MST.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Advances, the K-Contractive Map: A Supervised Version of Auto-CM
Nächstes Kapitel Auto-CM as a Dynamic Associative Memory
Literatur
1.
Strasser, B.J. 2012. Data Driven Sciences: From Wonder Cabinets to Electronic Databases. Studies in History and Philosophy of Biological and Biomedical Sciences 43: 85–87.CrossRef
2.
3.
Aflalo, Y., and R. Kimmel. 2013. Spectral Multidimensional Scaling. Proceedings of National Academy of Sciences 110: 18052–18057.MathSciNetCrossRefMATH
4.
Bro, R., and A.K. Smilde. 2014. Principal Component Analysis. Analytical Methods 6: 2812–2831.CrossRef
5.
Abdi, H., and L.J. Williams. 2010. Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2: 433–459.CrossRef
6.
Kuhnert, N., R. Jaiswal, P. Eravuchira, R.M. El-Abassy, B. von der Kammer, and A. Materny. 2011. Scope and Limitations of Principal Component Analysis of High Resolution of LC-TOF-MS Data: The Analysis of the Chlorogenic Acid Fraction in Green Coffee Beans as a Case Study. Analytical Methods 3: 144–155.CrossRef
7.
Kalyagin, V.A., A.P. Koldanov, P.M. Pardalos, and V.A. Zamaraev. 2014. Measures of Uncertainty in Market Network Analysis. Physica A 413: 59–70.MathSciNetCrossRef
8.
Kalyagin, V.A., A.P. Koldanov, and P.M. Pardalos. 2014. A General Approach to Network Analysis of Statistical Data Sets. Lecture in Computer Science, vol. 8426, 88–97. Berlin: Springer.
9.
10.
Buscema, M. 2007. Squashing Theory and Contractive Map Network. Semeion Technical Paper #32, Rome.
11.
Buscema, M., 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. Chapter 11, ed. V. Capecchi et al. Berlin: Springer Science + Business Media B.V.https://​doi.​org/​10.​1007/​978-90-481-8581-8_​11.
12.
Cormen, T.H., C.E. Leiserson, R.L. Rivest, and C. Stein. 2009. Introduction to Algorithms, 3rd ed. Cambridge MA: MIT Press.MATH
13.
Coelho, R., C.G. Gilmore, B. Lucey, P. Richmond, and S. Hutzler. 2007. The Evolution of Interdependence in World Equity Markets. Evidence from Minimum Spanning Trees. Physica A 376: 455–466.CrossRef
14.
Jang, W., J. Lee, and W. Chang. 2011. Currency Crises and the Evolution of Foreign Exchange Market: Evidence From Minimum Spanning Tree. Physica A 390: 707–718.CrossRef
15.
Zhang, Y., G.H.T. Lee, J.C. Wong, J.L. Kok, M. Prusty, and S.A. Cheong. 2011. Will the US Economy Recover in 2010? A Minimal Spanning Tree Study. Physica A 390: 2020–2050.CrossRef
16.
Dias, J. 2013. Spanning Trees and the Eurozone Crisis. Physica A 302: 5974–5984.CrossRef
17.
Sensoy, A., and B.M. Tabak. 2014. Dynamic Spanning Trees in Stock Market Networks: The Case of Asia-Pacific. Physica A 414: 387–402.CrossRef
18.
Buscema, M., and P. Luigi Sacco. 2016. MST Fitness Index and Implicit Data Narratives: A Comparative Test on Alternative Unsupervised Algorithms. Physica A 461: 726–746.CrossRef
19.
20.
Buscema, M., and W. Tastle. 2015. An Innovative Theory of Analysis: Artificial Neural Network What-If Theory. International Journal of Information Systems and Social Change 6: 52–81.CrossRef
21.
Hinton, G.E., S. Osindero, and Y.-W. Teh. 2006. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18: 1527–1554.MathSciNetCrossRefMATH
22.
23.
Maulik, U., and S. Bandyopadhyay. 2002. Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24: 1650–1654.CrossRef
24.
Duda, T., and M. Canty. 2002. Unsupervised Classification of Satellite Imagery: Choosing a Good Algorithm. International Journal of Remote Sensing 23: 2193–2212.CrossRef
25.
Giraudel, J.L., and S. Lek. 2001. A Comparison of Self-organizing Map Algorithm and Some Conventional Statistical Methods for Ecological Community Ordination. Ecological Modelling 146: 329–339.CrossRef
26.
Arsuaga Uriarte, E., and F. Diaz Martin. 2005. Topology Preservation in SOM. International Journal of Applied Mathematics and Computer Science 1: 19–22.
27.
Buscema, M. 1998. Constraint Satisfaction Neural Networks. Substance Use & Misuse 33: 389–408.CrossRef
28.
Consonni, V., and R. Todeschini. 2012. New Similarity Coefficients for Binary Data. MATCH Communications in Mathematical and in Computer Chemistry 68: 581–592.MathSciNet
29.
Jaccard, P. 1901. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37: 547–579.
30.
31.
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
32.
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.
33.
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 of Data Mining and Bioinformatics 2 (4): 362–404.CrossRef
34.
Buscema, M., E. Grossi, D. Snowdon, and P. Antuono. 2008. Auto-Contractive Maps: An Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease. Current Alzheimer Research 5: 481–498.CrossRef
35.
Buscema, M., C. Helgason, and E. Grossi. 2008. Auto Contractive Maps. H Function and Maximally Regular Graph: Theory and Applications. Special Session on Artificial Adaptive Systems in Medicine: Applications in the Real World. In NAFIPS 2008 (IEEE), New York. May 19–22, 2008.
36.
Licastro, F., E. Porcellini, M. Chiappelli, P. Forti, M. Buscema, et al. 2010. Multivariable Network Associated with Cognitive Decline and Dementia. Neurobiology of Aging 1: 257–269.CrossRef
37.
Grossi, E., G. TavanoBlessi, P.L. Sacco, and M. Buscema. 2012. The Interaction Between Cultures. Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project. Journal of Happiness Studies 13: 129–148.CrossRef
38.
Licastro, F., E. Porcellini, P. Forti, M. Buscema, I. Carbone, G. Ravaglia, and E. Grossi. 2010. Multi factorial Interactions in the Pathogenesis Pathway of Alzheimer’s Disease: A New Risk Charts for Prevention of Dementia. Immunity & Ageing 7: S4.CrossRef
39.
Buscema, M., F. Newman, E. Grossi, and W. Tastle. 2010. Application of Adaptive Systems Methodology to Radiotherapy. In NAFIPS 2010, July 12–14. Toronto, Canada.
40.
Eller-Vainicher, C., V.V. Zhukouskaya, Y.V. Tolkachev, S.S. Koritko, E. Cairoli, E. Grossi, P. Beck-Peccoz, I. Chiodini, and A.P. Shepelkevich. 2011. Low Bone Mineral Density and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic Statistics and Artificial Neural Network Analysis. Diabetes Care 34: 2186–2191.CrossRef
41.
Gomiero, T., L. Croce, E. Grossi, L. De Vreese, M. Buscema, U. Mantesso, and E. De Bastiani. 2011. A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems. US-China Education Review A 2: 196–207.
42.
Buscema, M., S. Penco, and E. Grossi. 2012. A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory. Neurology Research International (Art. ID 478560). https://​doi.​org/​10.​1155/​2012/​478560.
43.
Grossi, E., A. Compare, and M. Buscema. 2014. The Concept of Individual Semantic Maps in Clinical Psychology: A Feasibility Study on a New Paradigm. Quality & Quantity 48: 15–35.CrossRef
44.
45.
Street, M.E., M. Buscema, A. Smerieri, L. Montanini, and E. Grossi. 2013. Artificial Neural Networks and Evolutionary Algorithms as a Systems Biology Approach to a Data-Base on Fetal Growth Restriction. Progress in Biophysics and Molecular Biology 113: 433–438.CrossRef
46.
Compare, A., E. Grossi, M. Buscema, C. Zarbo, X. Mao, F. Faletra, E. Pasotti, T. Moccetti, P.M.C. Mommersteeg, and A. Auricchio. 2013. Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease. Cardiovascular Psychiatry and Neurology (Art. ID 814967). http://​dx.​doi.​org/​10.​1155/​2013/​814967.
47.
McClelland, J.L. 1981. Retrieving General and Specific Information from Stored Knowledge of Specifics. Proceedings of the Third Annual Meeting of the Cognitive Science Society 170–172.
48.
McClelland, J.L., D.E. Rumelhart, and G.E. Hinton. 1986. The Appeal of Parallel and Distributed Processing. In Parallel and Distributed Processing, vol. 1, ed. D.E. Rumelhart, and J.L. McClelland, 3–45. Cambridge, MA: MIT Press.
49.
Adobati, F., A. Azzini, F.C. Pavesi. 2011. Il ‘triangolo’ Milano-Bergamo-Brescia: verso nuovi equilibri sub-regionali. XXXII Conferenza Italiana di Scienze Regionali, Turin.
Metadaten
Titel
Comparison of Auto-CM to Various Other Data Understanding Approaches
verfasst von
Paolo Massimo Buscema
Giulia Massini
Marco Breda
Weldon A. Lodwick
Francis Newman
Masoud Asadi-Zeydabadi
Copyright-Jahr
2018
Buch
Artificial Adaptive Systems Using Auto Contractive Maps

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

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

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75049-1_7

Premium Partner

    Bildnachweise