A clustering device for clustering brain functional connectivity correlation values performing, based on measurement results of brain activities of objects, clustering of objects having at least one prescribed attribute among said objects, comprising a computing system for performing a process of said clustering based on measured values of brain activities, on a plurality of subjects including a first cohort of subjects having said prescribed attribute and a second cohort of subjects not having said prescribed attribute, said computing system including a storage device and a processor; wherein said processor is configured to perform the steps of i) storing, in said storage device, for each of said plurality of subjects, features based on a plurality of brain functional connectivity correlation values respectively representing time-wise correlation of brain activities between each of a prescribed plurality of pairs of brain regions, and ii) based on said features stored in said storage device, conducting supervised machine learning for generating an identifier model for discriminating presence/absence of said attribute; characterised in that said conducting supervised machine learning includes the steps of generating a plurality of training sub-samples by performing under-sampling and sub-sampling from said first cohort of subjects and said second cohort of subjects, and selecting, for each of said training sub-samples, from a sum set of features used for generating the identifier by machine learning, features for clustering in accordance with importance of features belonging to said sum set; and iii) clustering said first cohort of subjects through multiple co-clustering of unsupervised learning, based on the selected features for clustering, and thereby generating a cluster classifier. a computing system for performing a process of said clustering based on measured values of brain activities, on a plurality of subjects including a first cohort of subjects having said prescribed attribute and a second cohort of subjects not having said prescribed attribute, said computing system including a storage device and a processor; wherein said processor is configured to perform the steps of i) storing, in said storage device, for each of said plurality of subjects, features based on a plurality of brain functional connectivity correlation values respectively representing time-wise correlation of brain activities between each of a prescribed plurality of pairs of brain regions, and ii) based on said features stored in said storage device, conducting supervised machine learning for generating an identifier model for discriminating presence/absence of said attribute; characterised in that said conducting supervised machine learning includes the steps of generating a plurality of training sub-samples by performing under-sampling and sub-sampling from said first cohort of subjects and said second cohort of subjects, and selecting, for each of said training sub-samples, from a sum set of features used for generating the identifier by machine learning, features for clustering in accordance with importance of features belonging to said sum set; and iii) clustering said first cohort of subjects through multiple co-clustering of unsupervised learning, based on the selected features for clustering, and thereby generating a cluster classifier. i) storing, in said storage device, for each of said plurality of subjects, features based on a plurality of brain functional connectivity correlation values respectively representing time-wise correlation of brain activities between each of a prescribed plurality of pairs of brain regions, and ii) based on said features stored in said storage device, conducting supervised machine learning for generating an identifier model for discriminating presence/absence of said attribute; characterised in that said conducting supervised machine learning includes the steps of generating a plurality of training sub-samples by performing under-sampling and sub-sampling from said first cohort of subjects and said second cohort of subjects, and selecting, for each of said training sub-samples, from a sum set of features used for generating the identifier by machine learning, features for clustering in accordance with importance of features belonging to said sum set; and characterised in that said conducting supervised machine learning includes the steps of generating a plurality of training sub-samples by performing under-sampling and sub-sampling from said first cohort of subjects and said second cohort of subjects, and selecting, for each of said training sub-samples, from a sum set of features used for generating the identifier by machine learning, features for clustering in accordance with importance of features belonging to said sum set; and iii) clustering said first cohort of subjects through multiple co-clustering of unsupervised learning, based on the selected features for clustering, and thereby generating a cluster classifier.