The traditional model of grey nearness degree of incidence contains some inherent limitations in the calculation of data sequences. It does not consider the impacts of certain data on degree of incidence when there are significant differences in orders of magnitude between adjacent data in the same sequence, and big errors may occur in the calculation of some special oscillation sequences. In response to these problems, we propose a new improved method, which uses the characteristics of the model of grey nearness degree of incidence and introduces a neural network algorithm to define a grey neural network-nearness degree of incidence. Thereby, a model of nearness degree of incidence is established based on grey neural network. Then we apply a new model to the field of data mining. According to the clustering algorithm, we take all the degrees of incidence as the variables of the distance metric function, and use the clustering algorithm of data mining for data analysis. Finally, through simulation experiments, we verify the effectiveness of the clustering algorithm under the new distance metric definition. The experimental results show that, compared with other methods, the computational outcomes of the improved model are more consistent with the actual situation. The cluster algorithm with the model used can deliver results that have a high accuracy, so the new model can be applicated in a wide range of fields.

中文翻译：

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基于灰色神经网络的改进的关联度邻近度算法


传统的灰色关联度关联模型在数据序列的计算上存在一些固有的局限性。它没有考虑同一序列中相邻数据之间数量级差异较大时某些数据对发生程度的影响，在某些特殊振荡序列的计算中可能会出现较大误差。针对这些问题，我们提出了一种新的改进方法，该方法利用灰色贴近关联度模型的特点，引入神经网络算法来定义灰色神经网络贴近关联度。从而建立了基于灰色神经网络的发病贴近度模型。然后我们将一个新的模型应用到数据挖掘领域。根据聚类算法，将所有关联度作为距离度量函数的变量，利用数据挖掘的聚类算法进行数据分析。最后，通过仿真实验，验证了新距离度量定义下聚类算法的有效性。实验结果表明，与其他方法相比，改进模型的计算结果更加符合实际情况。该模型所采用的聚类算法可以得到高精度的结果，因此新模型可以应用于广泛的领域。