The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a minimum spanning tree based reduced space FJP algorithm is proposed. The computational experiments show that the reduced space algorithm enables the method to be used for much larger data sets.

中文翻译：

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一种空间有效的最小生成树模糊联合点聚类算法


模糊关节点（FJP）方法是一种基于邻域的聚类方法，它使用模糊邻域关系并消除了对参数的需要。尽管基于模糊邻域的聚类方法被证明足够快，可以在一秒内处理数万个数据，但空间复杂度仍然是一个限制因素。本研究提出了一种基于最小生成树的缩减空间FJP算法。计算实验表明，缩减空间算法使得该方法能够用于更大的数据集。