The clustering of big data streams has become a challenging task due to time and space constraints of the hardware and decreasing accuracy when the dimensionality of input data grows in time. In this paper, fuzzy growing neural gas is introduced, an online fuzzy approach for clustering data streams based on the growing neural gas algorithm, by adopting more restrictive criteria for selecting the winner nodes in the topological graph constructed at each iteration of the algorithm. The algorithm is tested on public datasets, and the results show improvements over existing clustering methods.

中文翻译：

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基于生长神经气体和模糊推理系统的数据流在线单遍聚类

由于硬件的时间和空间限制以及随着输入数据的维度随时间增长而降低的准确性，大数据流的聚类已成为一项具有挑战性的任务。本文介绍了模糊增长神经气体，一种基于增长神经气体算法的在线聚类数据流模糊方法，通过采用更严格的标准来选择算法每次迭代构建的拓扑图中的获胜节点。该算法在公共数据集上进行了测试，结果表明对现有聚类方法的改进。