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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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    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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).

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