Two-stage Aggregation Paradigm for \{HFLTS\} Possibility Distributions: A Hierarchical Clustering Perspective

TítuloTwo-stage Aggregation Paradigm for \{HFLTS\} Possibility Distributions: A Hierarchical Clustering Perspective
Tipo de publicaciónRevista Internacional
Año de publicación2018
AutoresZ-S. Chen, L. Martínez, K-S. Chin and K-L. Tsui
RevistaExpert Systems with Applications
Fecha Publicación08/2018
ISSN Number0957-4174
Palabras clavehierarchical clustering

Abstract The integration of possibility distribution into hesitant fuzzy linguistic term set (HFLTS) adds an extra dimension to individual opinion approximation process and significantly leads to enhanced data quality and reliability. However, aggregation of \{HFLTS\} possibility distributions involves merely associated possibilities of linguistic terms without taking into account all possible combinations of individual linguistic opinions. Therefore, computing with \{HFLTS\} possibility distributions in such a way has a high possibility of distorting final decisions due to loss of information. The introduction of hesitant 2-tuple linguistic term set (H2TLTS), which technically includes the \{HFLTS\} as a special case, offers us a different point of view in consolidating the aggregation process of HFLTSs. Due to the resemblance with H2TLTS, the alternative explantation of HFLTS, i.e., possibility distribution, can be analogously adapted to the theory of H2TLTS. By means of a conceptually simple recasting of \{HFLTS\} possibility distribution into a unified framework for \{H2TLTS\} possibility distribution with the development of possibilistic 2-tuple linguistic pair (P2TLP) concept, we develop a novel two-stage aggregation paradigm for \{HFLTS\} possibility distributions. At the first stage, the initial aggregation takes all possible combinations of \{P2TLPs\} in separate \{HFLTS\} possibility distributions together to generate an aggregated set of P2TLPs. Building on that, the subsequent stage proposes a similarity measure-based agglomerative hierarchical clustering (SM-AggHC) algorithm to reduce the cardinality of the aggregate set under consideration. The centroid approach combined with the normalization process finally guarantee the aggregation outcomes to be operated as \{H2TLTS\} possibility distributions.

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