The use of the hesitant fuzzy linguistic term sets (HFLTSs) has recently become an important trend in fuzzy decision making, and aggregating HFLTSs and their extensions has now become crucial for making decisions. Previous approaches to aggregating possibility distributions for HFLTSs were based on the paradigm of computing with words, whereas few proposals have been made to aggregate HFLTS possibility distributions under the framework of statistical data analysis so as to reduce information loss and distortion. An initial attempt was the similarity-measure-based agglomerative hierarchical clustering (SM-AggHC) two-stage aggregation paradigm for HFLTS possibility distributions, which, however, presents some important performance limitations from time complexity and memory requirement perspectives. Thereby, this paper introduces a new approach, so called, “$\mathcal{N}$-two-stage algorithmic aggregation paradigm driven by the K-means clustering” (N2S-KMC) to overcome these limitations by cardinality reduction in the first stage of the aggregation process. The subsequent stage uses the similarity-measure-based K-means clustering algorithm to outperform the SM-AggHC algorithm. Such an outperformance, from run time and memory usage, is demonstrated by experimental results.

犹豫模糊语言术语集（HFLTS）的使用最近已成为模糊决策的重要趋势，并且聚合 HFLTS 及其扩展现在已成为决策的关键。以前聚合 HFLTS 的可能性分布的方法是基于词计算的范式，而很少有人提出在统计数据分析的框架下聚合 HFLTS 可能性分布以减少信息丢失和失真。最初的尝试是用于 HFLTS 可能性分布的基于相似性度量的凝聚层次聚类 (SM-AggHC) 两阶段聚合范式，但是，从时间复杂度和内存需求的角度来看，它存在一些重要的性能限制。因此，本文介绍了一种新方法，$\mathcal{N}$-由K均值聚类驱动的两阶段算法聚合范式”（N2S-KMC）通过聚合过程第一阶段的基数减少来克服这些限制。后续阶段使用基于相似性度量的K均值聚类算法优于 SM-AggHC 算法。实验结果证明了这种从运行时间和内存使用情况来看的出色表现。