Publication Date
1985
Location
Kyoto Japan
Description
In this paper, two methods of Fuzzy Cluster Analysis-the A cut-set classification and the Interative Self-organization Data Analysis technique (ISODATA method)-are introduced to the classification of plant communities. The results show that using these methods, one may classify a number of sample quadrats obtained from vegetation mosaic which fails to possess definite lines among communities by means of random sampling rapidly and correctly. Moreover, it seems that the combination of the principal components analysis (PCA) and the ISODATA method in the classification is successful in reducing the subjective influence of the research worker and their results can be compared and redressed one another to make the result of communities classification more correct.
Citation
Cheng, Chen Qing, "An Application of Fuzzy Cluster Analysis in the Classification of Grazing Retrogressive Succession Stages of the Stipa Steppe" (1985). IGC Proceedings (1985-2023). 94.
(URL: https://uknowledge.uky.edu/igc/1985/ses6/94)
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
An Application of Fuzzy Cluster Analysis in the Classification of Grazing Retrogressive Succession Stages of the Stipa Steppe
Kyoto Japan
In this paper, two methods of Fuzzy Cluster Analysis-the A cut-set classification and the Interative Self-organization Data Analysis technique (ISODATA method)-are introduced to the classification of plant communities. The results show that using these methods, one may classify a number of sample quadrats obtained from vegetation mosaic which fails to possess definite lines among communities by means of random sampling rapidly and correctly. Moreover, it seems that the combination of the principal components analysis (PCA) and the ISODATA method in the classification is successful in reducing the subjective influence of the research worker and their results can be compared and redressed one another to make the result of communities classification more correct.
