Evaluating the effect of beta coefficient on the performance of flexible beta clustering in vegetation classification

Among different methods for classification, clustering is commonly used methods. Flexible-Beta clustering is successful hierarchical agglomerative clustering which is employed by ecologists as effective clustering method. The aim of the research was to detect the suitable value of beta for flexible-clustering methods. For this purpose, two different forest regions from Hyrcanian and Zagros Oak regions were selected. The clustering algorithms included Flexible-beta algorithms with five value of beta (-0.1, -0.25-, -0.4, -0.6 and -0.8). Five evaluators (Silhouette, MRPP, PARATNA, Phi coefficient) were employed on each cluster solution to evaluate different clustering algorithms. Algorithms were ranked from best to worst on each clustering evaluator for each data set. The results showed that Flexible-beta clustering with beta value -0.1 had best performance and Flexible-beta clustering with beta value -0.25 and -0.4 were proper performance in Hyrcanian regions. Flexible-beta clustering with beta value -0.25 was superior to others and Flexible-beta clustering with beta value -0.1 had the second rank. Since, choosing the most suitable clustering method is critical for achieving maximally ecological interpretable results, therefore, we suggested using flexible beta clustering with beta value equal to -0.1 and -0.25 in the studies area.