Fuzzy clustering and fuzzy validity measures for knowledge discovery and decision making in agricultural engineering

Abstract

This paper concerns the application of fuzzy clustering methods and fuzzy validity measures for decision support in agricultural environment. Data clustering methods, namely, K-Means, Fuzzy C-Means, Gustafson-Kessel, and Gath-Geva, are briefly reviewed and considered for analyses. The efficiency of the methods is determined by indices such as the Xie-Beni criterion, Partition Coefficient, and Partition and Dunn indices. In particular, fuzzy classifiers are developed to assist decision making regarding the control of variables such as bed moisture, temperature, and bed aeration in compost bedded pack barns. The idea is to identify interactive factors, promote cattle welfare, improve productivity indices, and increase property value. Data from 42 CBP barns in the state of Kentucky, US, were considered. Six classes related to the degree of efficiency of the composting process were identified. The GG method was the most accurate followed by the GK method. The main reason for the best results is the use of maximum-likelihood and Mahalanobis distance measures. A remark on the use of the Dunn validation index for different cluster geometries is given. Fuzzy models and linguistic information have shown to be useful to help decision making in cattle containment systems.