  We propose a data-driven method to extract dissimilarity between materials,
with respect to a given target physical property. The technique is based on an
ensemble method with Kernel ridge regression as the predicting model; multiple
random subset sampling of the materials is done to generate prediction models
and the corresponding contributions of the reference training materials in
detail. The distribution of the predicted values for each material can be
approximated by a Gaussian mixture model. The reference training materials
contributed to the prediction model that accurately predicts the physical
property value of a specific material, are considered to be similar to that
material, or vice versa. Evaluations using synthesized data demonstrate that
the proposed method can effectively measure the dissimilarity between data
instances. An application of the analysis method on the data of Curie
temperature (TC) of binary 3d transition metal 4f rare earth binary alloys also
reveals meaningful results on the relations between the materials. The proposed
method can be considered as a potential tool for obtaining a deeper
understanding of the structure of data, with respect to a target property, in
particular.
