Characterizations Of Nearest And Farthest Neighbor Algorithms By Clustering Admissibility Conditions

Abstract Monotone admissibility for clustering algorithms was introduced in Fisher and Van Ness [Biometrika 58, 91–104 (1971)]. The present paper discusses monotone admissibility for a broad class of clustering algorithms called the Lance and Williams algorithms. Necessary and sufficient conditions for Lance and Williams algorithms to be monotone admissible are discussed here. It is shown that the only such algorithms which are monotone admissible are nearest neighbor and farthest neighbor.