This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

中文翻译：

---

基于自组织地图网络的聚类集成模型。

本文提出了一种聚类集成方法，将级联结构引入自组织图（SOM），以解决单个聚类器性能较差的问题。级联SOM是结合了级联结构的经典SOM的扩展。该方法以级联的方式组合多个SOM网络的输出，并将它们用作另一个SOM网络的输入。它还利用了高维数据对少数维的值不敏感的特性，从而获得了忽略部分SOM网络错误输出的效果。由于SOM网络的初始参数和样本训练顺序是随机生成的，因此该模型无需为每个SOM网络提供不同的训练样本即可生成差异化的SOM聚类器。