Method and device for the generation of a classification tree to unify the supervised and unsupervised approaches, corresponding computer package and storage means

A computer-readable storage memory, which may be totally or partially detachable, comprising a set of instructions stored thereon and executable by said computer to implement a method for the generation of a classification tree as a function of a classification model and from a set of data to be classified, described by a set of attributes, said set of data being a set of documents of a documentary database,

• wherein the method comprises obtaining said classification model, the classification model comprising defining a mode of use for each attribute, which comprises:
• marking said attribute “target” if the attribute has to be explained, or “not target” if the attribute has not to be explained;
• and
• marking said attribute “taboo” if the attribute has not to be used as explanatory, or “not taboo” if the attribute has to be used as explanatory;
• “target” and “taboo” being two properties not exclusive of each other,
• and wherein, said classification model belongs to the group comprising:
• supervised classification models with one target attribute, in each of which a single attribute is marked target and taboo, the attributes that are not marked target being not marked taboo;
• supervised classification models with several target attributes, in each of which at least two attributes, but not all the attributes, are marked target and taboo, the attributes not marked target being not marked taboo;
• and
• unsupervised classification models, in each of which all the attributes are marked target and at least one attribute is not marked taboo.

A device for the generation of a classification tree as a function of a classification model and from a set of data to be classified, described by a set of attributes, said set of data being a set of documents of a documentary database, wherein the device comprises a memory and means for obtaining said classification model, the means themselves comprising means to define a mode of use for each attribute which comprise:

• means for marking said attribute “target” if the attribute has to be explained, or “not target” if the attribute has not to be explained:
• and
• means for marking said attribute “taboo” if the attribute has not to be used as explanatory, or “not taboo” if the attribute has to be used as explanatory,
• “target” and “taboo” being two properties not exclusive of each other, and wherein, said classification model belongs to the group comprising:
• supervised classification models with one target attribute, in each of which a single attribute is marked target and taboo, the attributes that are not marked target being not marked taboo;
• supervised classification models with several target attributes, in each of which at least two attributes, but not all the attributes, are marked target and taboo, the attributes not marked target being not marked taboo;
• and
• unsupervised classification models, in each of which all the attributes are marked target and at least one attribute is not marked taboo.