Clustering alternatives in preference-approvals via novel pseudometrics

Abstract

Preference-approval structures combine preference rankings and approval voting for declaring opinions over a set of alternatives. In this paper, we propose a new procedure for clustering alternatives in order to reduce the complexity of the preference-approval space and provide a more accessible interpretation of data. To that end, we present a new family of pseudometrics on the set of alternatives that take into account voters’ preferences via preference-approvals. To obtain clusters, we use the Ranked k-medoids (RKM) partitioning algorithm, which takes as input the similarities between pairs of alternatives based on the proposed pseudometrics. Finally, using non-metric multidimensional scaling, clusters are represented in 2-dimensional space.

Soil Environments Regulate Dominant Soil Fungal Communities along an Elevational Gradient in Subtropical Forests

“…This method was used to investigate differences in soil fungal community structure along the elevational gradient. Furthermore, the application of non-metric multidimensional scaling (NMDS) helped assess the differences in dominant soil fungal community structures at different elevations through the Permanova method and helped precisely quantify the contribution of influencing factors to explain the diversity of soil fungal communities, thereby reinforcing the hypothesis of elevation-related diversity patterns [26,27].…”