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The rate of brucellosis, a zoonotic disease, has rapidly increased in humans brucellosis(HB) in recent years. In 1950–2018, a total of 684,380 HB cases (median 2274/year (interquartile range (IQR) 966–8325)) were reported to the National Infectious Disease Surveillance System in mainland China. The incidence of HB peaked in 2014 (4.32/100,000), and then showed a downward trend; we predict that it will maintain a steady downward trend in 2019–2020. Since 2015, the incidence of HB has shown opposite trends in the north and south of China; rates in the north have fallen and rates in the south have increased. In 2004–2018, the most significant increases in incidence of HB were in Yunnan (IQR 0.002–0.463/100,000), Hubei (IQR 0.000–0.338/100,000), and Guangdong (IQR 0.015–0.350/100,000). The areas where HB occurs have little overlap with areas with high per capita GDP in China. The “high–high” clusters of HB are located in northeastern China (Inner Mongolia, Heilongjiang, Jilin, Liaoning, Ningxia, Shanxi, and Gansu), and the “low–low” clusters of HB are located in southern China (Yunnan, Jiangxi, Shanghai, Guangxi, Guangdong, Zhejiang, Guizhou, and Hunan). In recent years, the incidence of HB in China has been controlled to some extent, but the incidence of HB has increased in southern China, and the disease has spread geographically in China from north to south. Further research is needed to address this change and to continue to explore the relationship between the incidence of HB and relevant factors.

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Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach. By theoretical analysis, we prove that a general form of RPCL, called distance-sensitive RPCL (DSRPCL), is associated with the minimization of a cost function on the weight vectors of a competitive learning network. As a DSRPCL process decreases the cost to a local minimum, a number of weight vectors eventually fall into a hypersphere surrounding the sample data, while the other weight vectors diverge to infinity. Moreover, it is shown by the theoretical analysis and simulation experiments that if the cost reduces into the global minimum, a correct number of weight vectors is automatically selected and located around the centers of the actual clusters, respectively. Finally, we apply the DSRPCL algorithms to unsupervised color image segmentation and classification of the wine data.

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