naghmeh pakgohar

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.

Vegetation classification is an essential tool to describe, understand, predict and manage ecosystems. The aim of this study was to compare different types of hierarchical clustering. Three forest patches with similar slope and altitude gradients located on the southern slopes of Chahar Zebar forests, Kermanshah province, were selected. Vegetation sampling in each patch was conducted at 0, 25, 50, 100 and 150-meter distances along three transects that were 200 m apart. Cluster analysis was used for the classification of samples. Amongst the applied methods, Gower’s distance (or similarity) initially computes distances between pairs of variables over data sets and then merges those distances with the nearest neighbor, complete neighbor, average neighbor, and Ward’s method. The optimal number and quality of clusters were evaluated with silhouette criteria. In addition, the Cophenetic correlation coefficient was computed for evaluating the correlation between the dendrogram and the distance matrix. Results showed that two was the optimal number of clustering for oak stands. Moreover, the Cophenetic correlation coefficient between the distance matrix and the nearest neighbor and average method was higher than that returned between complete neighbor and Ward’s method. Based on silhouette criteria, the nearest neighbor and average methods were associated with higher cluster quality compared with two other methods. However, the mean value of the silhouette index was low for the second cluster of the nearest neighbor method. Considering the disadvantages of the nearest neighbor, the average method is suggested for clustering categorical data.