km#conceptual_clustering__concept_clusterization__conceptclusterization  it can be used both for KA and IR, from knowledge or data
  supertype:  km#classification
  subtype:  km#conceptual_clustering_via_a_generalization_hierarchy
     subtype:  km#conceptual_clustering_via_a_category_generalization_hierarchy
        subtype:  km#FCA_based_conceptual_clustering
           subtype:  km#FCA_attribute_exploration  FCA technique addressing the problem of a context where the object set is not completely known a priori, or too large to be listed
           subtype:  km#FCA_concept_exploration  FCA technique addressing the problem of a context where both the object set and the attribute set are not completely known a priori, or too large to be listed
        subtype:  km#type_classification
        subtype:  km#instance_classification__instance_learning  assignement of instances to types of concepts/relations
     subtype:  km#conceptual_clustering_via_a_CG_generalization_hierarchy
  subtype:  km#conceptual_clustering_from_data
     subtype:  km#conceptual_clustering_from_database
     subtype:  km#conceptual_clustering_from_documents
        subtype:  km#conceptual_clustering_from_emails

7 statements are about indirect instances of km#conceptual_clustering: km#graph13_on_article, km#graph15_on_article, km#graph18_on_article, km#graph1_on_PhD_thesis, km#graph2_on_PhD_thesis, km#graph25_on_article, km#graph34_on_article
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