A Method for Fish Diseases Diagnosis Based on Rough Set and FCM Clustering Algorithm

In order to achieve the rapid and mass diagnosis of fish diseases, it is proposed to set up a new and efficient model which closely connects rough set and Fuzzy C-means(FCM) clustering algorithm. First, the rough set was used for access to knowledge, that is, the typical cases of fish diseases were regarded as sample room for the formation of the decision-making table of the “symptoms - disease”; next, based on rough set of simplified method of knowledge, redundant properties and samples were removed; then, the fine performance of FCM clustering algorithm was used to analyze clustering; and finally fish diseases classification rules were formed. The model integrated the strong extracting capabilities of rough set and the excellent classifying ability of FCM clustering algorithm, and proved experimentally to be efficient in classification and rapid in fish diseases diagnosis.