Comparison of two non-hierarchal clustering performance in vegetation community datasets

Document Type : Research article

Authors

1 Ph.D. of Forestry, Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran

2 Corresponding author, Prof., Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran

3 Assistant Prof., Department of Mathematics, Faculty of Science, Urmia University, Urmia, Iran

4 Associate Prof., Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran

5 Prof., Department of Ecology, Montana State University, Bozeman, USA

Abstract

     Clustering task is optimized and summarized high dimensional vegetation datasets that indicator of environmental change and gathering to interpreting pattern form ecosystem. Variety clustering methods is available and the issue is chosen proper methods. The aim of the research was compared two non-hierarchical clustering as K-means and K-medoids in forest ecosystems. For this purpose, two real datasets from Hyrcanian and Zagros forests of Iran and six simulated datasets were applied. The Hellinger transformation was employed before calculating dissimilarity matrices. Euclidean distance, Manhattan distance and Bray-Curtis dissimilarity indices were then calculated on the transformed data sets. And three evaluators including silhouette width, phi coefficient and ISAMIC were chosen. The results show that combination of Bray-Curtis dissimilarity matrices and K-means and K-medoids have first and second ranks among other clustering methods. K-means clustering is more effective in heterogenous dataset as Zagros and simulated datasets. The weakest clustering algorithm was combination between Manhattan distance and K-medoids. Also results show that Hellinger data transformation cause to improve Euclidean distance matrix. Our results indicated that combination of Bray-Curtis dissimilarity with K-means is more significant and recommended.

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