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MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms

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  • Buscema, Massimo
  • Sacco, Pier Luigi

Abstract

In this paper, we introduce a new methodology for the evaluation of alternative algorithms in capturing the deep statistical structure of datasets of different types and nature, called MST Fitness, and based on the notion of Minimum Spanning Tree (MST). We test this methodology on six different databases, some of which artificial and widely used in similar experimentations, and some related to real world phenomena. Our test set consists of eight different algorithms, including some widely known and used, such as Principal Component Analysis, Linear Correlation, or Euclidean Distance. We moreover consider more sophisticated Artificial Neural Network based algorithms, such as the Self-Organizing Map (SOM) and a relatively new algorithm called Auto-Contractive Map (AutoCM). We find that, for our benchmark of datasets, AutoCM performs consistently better than all other algorithms for all of the datasets, and that its global performance is superior to that of the others of several orders of magnitude. It is to be checked in future research if AutoCM can be considered a truly general-purpose algorithm for the analysis of heterogeneous categories of datasets.

Suggested Citation

  • Buscema, Massimo & Sacco, Pier Luigi, 2016. "MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 726-746.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:726-746
    DOI: 10.1016/j.physa.2016.05.055
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    1. Miccichè, Salvatore & Bonanno, Giovanni & Lillo, Fabrizio & N. Mantegna, Rosario, 2003. "Degree stability of a minimum spanning tree of price return and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 66-73.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Enzo Grossi & Angelo Compare & Massimo Buscema, 2014. "The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 15-35, January.
    4. Dias, João, 2013. "Spanning trees and the Eurozone crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5974-5984.
    5. C. Coronnello & M. Tumminello & F. Lillo & S. Miccich`e & R. N. Mantegna, 2005. "Sector identification in a set of stock return time series traded at the London Stock Exchange," Papers cond-mat/0508122, arXiv.org.
    6. Paolo Massimo Buscema & William J. Tastle, 2015. "Artificial Neural Network What-If Theory," International Journal of Information Systems and Social Change (IJISSC), IGI Global, vol. 6(4), pages 52-81, October.
    7. Enzo Grossi & Giorgio Tavano Blessi & Pier Sacco & Massimo Buscema, 2012. "The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project," Journal of Happiness Studies, Springer, vol. 13(1), pages 129-148, March.
    8. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    9. Yiting Zhang & Gladys Hui Ting Lee & Jian Cheng Wong & Jun Liang Kok & Manamohan Prusty & Siew Ann Cheong, 2010. "Will the US Economy Recover in 2010? A Minimal Spanning Tree Study," Papers 1009.5800, arXiv.org, revised Dec 2010.
    10. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    11. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A. & Pardalos, P.M. & Zamaraev, V.A., 2014. "Measures of uncertainty in market network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 59-70.
    12. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
    13. Coelho, Ricardo & Gilmore, Claire G. & Lucey, Brian & Richmond, Peter & Hutzler, Stefan, 2007. "The evolution of interdependence in world equity markets—Evidence from minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 455-466.
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    Cited by:

    1. Erspamer, Christopher & Della Torre, Francesca & Massini, Giulia & Ferilli, Guido & Sacco, Pier Luigi & Buscema, Paolo Massimo, 2022. "Global world (dis-)order? Analyzing the dynamic evolution of the micro-structure of multipolarism by means of an unsupervised neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Buscema, Massimo & Ferilli, Guido & Gustafsson, Christer & Massini, Giulia & Sacco, Pier Luigi, 2022. "A nonlinear, data-driven, ANNs-based approach to culture-led development policies in rural areas: The case of Gjakove and Peć districts, Western Kosovo," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Paolo Massimo Buscema & Guido Ferilli & Christer Gustafsson & Pier Luigi Sacco, 2020. "The Complex Dynamic Evolution of Cultural Vibrancy in the Region of Halland, Sweden," International Regional Science Review, , vol. 43(3), pages 159-202, May.
    4. Mennini, Francesco Saverio & Gitto, Lara & Russo, Simone & Cicchetti, Americo & Ruggeri, Matteo & Coretti, Silvia & Maurelli, Guido & Buscema, Paolo Massimo, 2017. "Does regional belonging explain the similarities in the expenditure determinants of Italian healthcare deliveries?," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 47-56.
    5. De Carlo, Manuela & Ferilli, Guido & d'Angella, Francesca & Buscema, Massimo, 2021. "Artificial intelligence to design collaborative strategy: An application to urban destinations," Journal of Business Research, Elsevier, vol. 129(C), pages 936-948.
    6. Paolo Massimo Buscema & Francesca Della Torre & Giulia Massini & Guido Ferilli & Pier Luigi Sacco, 2023. "Reconstructing the Emergent Organization of Information Flows in International Stock Markets: A Computational Complex Systems Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 49-89, June.
    7. Buscema, Massimo & Ferilli, Guido & Sacco, Pier Luigi, 2017. "What kind of ‘world order’? An artificial neural networks approach to intensive data mining," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 46-56.

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