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The proposal is novel for the methodology used, a spatial Fuzzy clustering model for players and for tournaments (based on related attributes), where the spatial penalty term in each clustering model depends on the relation between players and tournaments described in the adjacency matrix. The proposed model is compared with a bipartite players-tournament complex network model (the Degree-Corrected Stochastic Blockmodel) that considers only the relation between players and tournaments, described in the adjacency matrix, to obtain communities on each side of the bipartite network. An application on data taken from the ATP official website with regards to the draws of the tournaments, and from the sport statistics website Wheelo ratings for the performance data of players and tournaments, shows the performances of the proposed clustering model.<\/jats:p>","DOI":"10.1007\/s00180-024-01493-2","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T08:02:01Z","timestamp":1713254521000},"page":"1689-1712","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Network and attribute-based clustering of tennis players and tournaments"],"prefix":"10.1007","volume":"40","author":[{"given":"Pierpaolo","family":"D\u2019Urso","sequence":"first","affiliation":[]},{"given":"Livia","family":"De Giovanni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-3901","authenticated-orcid":false,"given":"Lorenzo","family":"Federico","sequence":"additional","affiliation":[]},{"given":"Vincenzina","family":"Vitale","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"issue":"1","key":"1493_CR1","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s10479-022-04594-7","volume":"325","author":"A Arcagni","year":"2023","unstructured":"Arcagni A, Candila V, Grassi R (2023) A new model for predicting the winner in tennis based on the eigenvector centrality. 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