Red tides are caused by the combination effects of many marine elements. The complexity of the marine ecosystem makes it hard to find the relationship between marine elements and red tides. The algorithm of fuzzyc-means (FCM) can get clear classification of things and expresses the fuzzy state among different things. Therefore, a prediction algorithm of red tide based on improved FCM is proposed. In order to overcome the defect of FCM which is overdependent on the initial cluster centers and the objective function, this paper gains the initial cluster centers through the principle of regional minimum data density and the minimum mean distance. The feature weighted cluster center is added to the objective function. Finally, the improved FCM algorithm is applied in the prediction research of red tide, and the results show that the improved FCM algorithm has good denoising ability and high accuracy in the prediction of red tides.
The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.
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