Nowadays, the application of clustering methods is widely increased, although choosing the right method due to the existence of different method and effective factors is difficult. The present study aimed to compare the results of widely used clustering algorithms and t
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Nowadays, the application of clustering methods is widely increased, although choosing the right method due to the existence of different method and effective factors is difficult. The present study aimed to compare the results of widely used clustering algorithms and to determine the most effective methods according to the different evaluators and evaluate the effective distance measurement method for clustering algorithms. The data of Hyrcanian beech forests were examined in an area protected by the department of natural resources of Nowshahr. Random-systematic sampling method with regular grid of 100×200 m was used for determining the center of sample plots; 100-m2 (10×10 m) sample plots had been used to check the shrub species and 400-m2 (20×20 m) to check the herbaceous species. A total of 120 sample plots were measured. The abundance and coverage of tree, shrub and herbaceous species were estimated based on Braun-Blanquette scale. Three distance methods of measuring distance Bray Curtis, Hellinger and Manhattan were used and five clustering methods (Average method clustering methods, Ward method, flexible beta method with beta values of -0.1, -0.25, -0.4) with six evaluation indicators (silhouette evaluation criterion, PARATNA criterion, Indval criterion, ISAMIC criterion, MRPP criterion and Phi correlation coefficient) were examined. Different clustering algorithms were arranged from best to worst for each dataset. The comparison analysis revealed that Ward’s and flexible-beta with beta value of -0.1 had the best performance. The present findings illustrated that Hellinger distance measurement method is better in homogeneous data than other distance measurement methods.
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