Abstract: A non-transitory computer-readable recording medium stores therein a feature value calculation program that causes a computer to execute a process. The process includes calculating, for each of points included in point group data, an eigenvector by using principal component analysis on point group data that is located within a predetermined distance from each of the points; calculating a curvature of a multivariable function in which a point located closest to the calculated eigenvector is used an extreme value point; and generating a feature value of the point group data on the basis of the curvature at each of the points in the point group data.

Abstract: A non-transitory computer-readable recording medium stores therein a data clustering program that causes a computer to execute a process. The process includes calculating, for each of plural points included in a set of point group data, an eigenvector by using principal component analysis for a set of point group data that is present within a predetermined distance from a point; calculating a curvature of a multidimensional function having an extreme point that is a point positioned nearest to the eigenvector calculated; executing, on the basis of the curvature for each of the plural points of the set of point group data, clustering of the plural points; and outputting a result of execution of the clustering.

Abstract: A non-transitory computer-readable storage medium storing an estimation program that causes a computer to execute a process includes specifying representative points of each of training clusters that corresponds to each of labels targeted for estimation; setting boundaries between each of input clusters under a condition that a number of the input clusters and a number of the representative points coincide with each other, the input clusters being generated by clustering in a feature space for input data; acquiring estimation results for the labels with respect to the input data based on a correspondence relationship between the input clusters and the training clusters based on the boundaries; and estimating determination accuracy for the labels by using the machine learning model with respect to the input data based on the estimation results.

Abstract: A program causes the processor to: estimate, a determination result of a model for performing determination based on attribute values corresponding to attributes related to a target, a degree of correlation of each of combination patterns with the determination result, each combination pattern being a combination that includes attributes selected from attributes satisfying a predetermined condition among the attributes and attributes selected from attributes other than the attributes satisfying the predetermined condition among the attributes, and estimate, based on a difference between a first degree of correlation of a first combination pattern among the combination patterns with the determination result, and a second degree of correlation of a second combination pattern that is a combination pattern obtained by removing a first attribute among the attributes satisfying the predetermined condition from the first combination pattern with the determination result, a degree of influence of the first attribute