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Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters


  • Francisco Benita
  • Serhad Sarica
  • Garvit Bansal


This paper proposes a new framework to test the static and dynamic performance of clustering techniques for the detection of national industrial clusters. Principal component analysis (PCA), latent profile analysis (LPA), Louvain community detection algorithm (Louvain), and fuzzy analysis clustering (Fuzzy) are compared. It also proposes a Penalized Dunn Index as a novel metric for assessing the quality of industrial cluster dynamics. The findings indicate that PCA results in large variations in the total number of clusters among countries, but industrial clusters are not consistent over time. LPA and Fuzzy algorithms perform well in the case of static datasets, whereas Louvain offers a good balance between cluster diversity and dynamic consistency. En este artículo se propone un nuevo marco para poner a prueba el desempeño estático y dinámico de las técnicas de clustering para la detección de conglomerados (clusters) industriales nacionales. Se compara el análisis de componentes principales (ACP), el análisis de perfil latente (APL), el algoritmo de Louvain de detección de comunidades y el clustering de análisis difuso (Fuzzy). También se propone un Índice de Dunn Penalizado como una métrica novedosa para evaluar la calidad de la dinámica de conglomerados (clusters) industriales. Los resultados indican que el ACP da lugar a grandes variaciones en el número total de conglomerados entre países, pero que los conglomerados industriales no son uniformes a lo largo del tiempo. Los algoritmos APL y Fuzzy funcionan bien en el caso de conjuntos de datos estáticos, mientras que el de Louvain ofrece un buen equilibrio entre la diversidad de conglomerados y la uniformidad dinámica. 本稿では、国内の産業クラスターを検出するクラスター分析法の、静学的および動学的パフォーマンスをテストする新しいフレームワークを提案する。主成分分析 (principal component analysis : PCA)、潜在プロファイル分析 (latent profile analysis: LPA)、コミュニティ抽出アルゴリズムのLouvain法 (Louvain community detection algorithm: Louvain)、ファジィクラスタリング (fuzzy analysis clustering: Fuzzy)、以上の方法を比較した。知見から、PCAの結果では、国によってクラスターの総数に大きなばらつきが認められたが、産業クラスターの数は経時的には一定ではないことが示された。LPAおよびFuzzyのアルゴリズムは、静学的データセットの場合では良好なパフォーマンスであったが、Louvainは、クラスターの多様性と動学的整合性のバランスが良好であった。

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  • Francisco Benita & Serhad Sarica & Garvit Bansal, 2020. "Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1137-1157, August.
  • Handle: RePEc:bla:presci:v:99:y:2020:i:4:p:1137-1157
    DOI: 10.1111/pirs.12517

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    References listed on IDEAS

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