K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm

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, “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.