This research was performed to define genetic diversity of wild red currant accessions native to Eastern Anatolia region of Turkey by revealing associations between physicochemical berry characteristics through various statistical methods including Explanatory factor analysis, hierarchical cluster analysis (single linkage-Euclidian distance) and fuzzy clustering (Manhattan distance). The factor analysis explained 89% of the data variability on the tested berry features by four factors. The first factor was called berry color and showed positive loadings on A (0.947), B (0.925) and negative loading on L (− 0.909). The second factor was named organoleptic which had positive loadings on aroma (0.993) and taste (0.993). The third factor was called pomology and pH and demonstrated positive loadings on fruit weight (0.903) and fruit length (0.824), but negative loading on pH (− 0.583). The fourth factor was named soluble solid content and exhibited positive loading of 0.928. The hierarchical cluster analyses resulted with seven clusters showing 83.89 (%), 83.33 (%), 80.71 (%), 88.21 (%), 91.28 (%), 95.12 (%) and 90.37 (%) similarity for the first, second, third, fourth, fifth, sixth and seventh clusters, respectively. The largest values of average silhouette and Dunn’s partition coefficient, as well as the smallest value of the normalized partition coefficient of fuzzy clustering analysis resulted in five clusters. Furthermore, a hybrid approach combining fuzzy clustering and decision tree algorithms was established to better characterize the phenotypical profile of red currants. We can conclude that the statistical methods utilized in this research could be a useful tool in revealing phenotypic similarities and differences among red currant accessions, and the knowledge on berry feature associations could be helpful in plant breeding programs.

本研究旨在通过解释性因素分析、层次聚类分析（单链接-欧几里得距离）和模糊聚类等各种统计方法揭示浆果物理化学特征之间的关联，从而定义原产于土耳其东安纳托利亚地区的野生红醋栗种质的遗传多样性。曼哈顿距离）。因子分析通过四个因子解释了测试浆果特征的 89% 的数据变异性。第一个因素称为浆果颜色，在 A (0.947)、B (0.925) 上显示正负载，在 L (- 0.909) 上显示负负载。第二个因素被命名为感官，它对香气 (0.993) 和味道 (0.993) 有积极的影响。第三个因素被称为果树学和 pH 值，显示出对果实重量 (0.903) 和果实长度 (0.824) 的正负荷，但对 pH 值负负荷（- 0.583）。第四个因素被命名为可溶性固体含量，其正负荷为 0.928。分层聚类分析结果有七个聚类，显示第一个、第二个、83.89 (%)、83.33 (%)、80.71 (%)、88.21 (%)、91.28 (%)、95.12 (%) 和 90.37 (%) 的相似性，分别为第三、第四、第五、第六和第七组。模糊聚类分析的平均轮廓和邓恩分配系数的最大值以及归一化分配系数的最小值产生了五个聚类。此外，建立了一种结合模糊聚类和决策树算法的混合方法，以更好地表征红醋栗的表型特征。