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extended clustering algorithm based on role representation (and hierarchical clustering)  (NARIZUKA Corporation)

 
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    NARIZUKA Corporation extended clustering algorithm based on role representation (and hierarchical clustering)
    Extended Clustering Algorithm Based On Role Representation (And Hierarchical Clustering), supplied by NARIZUKA Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/hierarchical-based+clustering+algorithm/delaunay+network/pm37092371-96-86-84
    Average 90 stars, based on 1 article reviews
    extended clustering algorithm based on role representation (and hierarchical clustering) - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Identifying Soccer Teams' Styles of Play: A Scoping and Critical Review.
    Article Snippet: Narizuka and Yamazaki [57] adopted the Delaunay network to get (as adjacency matrix A(t)) the formation of a team at time t. Then, using hierarchical clustering, they obtained not only the average formation (i.e., “442”, “4141”, “433”, “541” or “343”) for each team in the match but also the positional exchange of players within the match formations.

    Article Title: Identifying Soccer Teams' Styles of Play: A Scoping and Critical Review.
    Article Snippet: Author Year C at eg or y Method Kind of Data Sample (Number of Matches & Competitions) P.I. (Event) Tracking Data Other Bialkowski [54] 2016 k-means clustering, agglomerative clustering, linear discriminant analysis, k-nearest neighbour * * 374 one league Brooks [55] 2016 K-nearest neighbor, L2-regularized support vector machine model Every pass (with 8 descriptors) _ 2012–2013 La Liga Bekkers [56] 2019 Mean shift algorithm Flow motifs 8219 4 seasons (2012/2013 to 2015/2016), 6 different leagues (Dutch, English, Spanish, Italian, French and German first division) Narizuka [57] 2019 Extended clustering algorithm based on role representation (and hierarchical clustering) * 45 Japanese league 2016 Decroos [58] 2020 Mixture models Actions described by their type, location, and direction 760 2017/18 and 2018/19seasons of the EPL Beernaerts [59] 2020 QualitativeTrajectory Calculus * 1 2016–2017 professional soccer competition GarcíaAliaga [60] 2022 t-SNE dimensionality reduction technique, classification rules with RIPPER * all EPL, Spanish LaLiga, German Bundesliga, and Italian Serie A from the 2014/2015 to 2018/2019 seasons Lee [61] 2022 Deep Neural Networks (DNN) based on Multi-Layer Perceptron (MLP) and feature engineering * (a) all Tottenham’s games, (b) 380 (a) 11 seasons (2010/2011–2020/2021) English premier league, (b) 2020-21 EPL Amatria [62] 2019 C.I.S.

    Article Title: Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review
    Article Snippet: Narizuka [ ] , 2019 , , Extended clustering algorithm based on role representation (and hierarchical clustering) , , * , , 45 , Japanese league 2016.

    Article Title: Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review
    Article Snippet: Narizuka and Yamazaki [ ] adopted the Delaunay network to get (as adjacency matrix A(t)) the formation of a team at time t. Then, using hierarchical clustering, they obtained not only the average formation (i.e., “442”, “4141”, “433”, “541” or “343”) for each team in the match but also the positional exchange of players within the match formations.

    Article Title: Identifying Soccer Teams' Styles of Play: A Scoping and Critical Review.
    Article Snippet: Narizuka and Yamazaki [57] adopted the Delaunay network to get (as adjacency matrix A(t)) the formation of a team at time t. Then, using hierarchical clustering, they obtained J. Funct.

    Article Title: Method for providing tactical information related to team sports
    Article Snippet: Narizuka and Yamazaki's paper “Characterization of the formation structure in team sports (2017)” proposes a method of representing a temporary formation as a non-directional graph using Delaunay triangulation and clustering graphs for a single game to characterize a formation structure.



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