农业工程外文文献 Fuzzy clustering and fuzzy validity measures for knowledge discovery and decision making in agricultural engineering.pdf

Computers and Electronics in Agriculture 150 (2018) 118–124 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: /locate/compag Original papers Fuzzy clustering and fuzzy validity measures for knowledge discovery and T decision making in agricultural engineering Vania C. Mota, Flavio A. Damasceno, Daniel F. Leite⁎ Department of Engineering, Federal University of Lavras (UFLA), Lavras, Minas Gerais 37200-000, Brazil A R T I C L E I N F O A B S T R A C T Keywords: This paper concerns the application of fuzzy clustering methods and fuzzy validity measures for decision support Fuzzy clustering in agricultural environment. Data clustering methods, namely, K-Means, Fuzzy C-Means, Gustafson-Kessel, and Pattern recognition Gath-Geva, are briefly reviewed and considered for analyses. The efficiency of the methods is determined by Fuzzy validation measure indices such as the Xie-Beni criterion, Partition Coefficient, and Partition and Dunn indices. In particular, fuzzy Decision support system classifiers are developed to assist decision making regarding the control of variables such as bed moisture, Agricultural engineering temperature, and bed aeration in compost bedded pack barns. The idea is to identif