Hua Wenqi, Mo Lingfei
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Comput Intell Neurosci. 2020 Aug 25;2020:2971565. doi: 10.1155/2020/2971565. eCollection 2020.
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%.
本文提出了一种聚类集成方法，该方法将级联结构引入自组织映射（SOM）以解决单个聚类器性能不佳的问题。级联SOM是结合了级联结构的经典SOM的扩展。该方法以级联方式组合多个SOM网络的输出，并将其用作另一个SOM网络的输入。它还利用高维数据对少数维度值变化不敏感的特性，以达到忽略部分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以生成差异化的SOM聚类器。在几个经典数据集上进行测试后，实验结果表明该模型可以有效地将模式识别准确率提高4%至10%。
