Patent Document ID: 7584168
Application ID: 11354265

Base Claim:
1. A method comprising: generating with a computer 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; 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.

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Claim 4:
4. A method according to claim 1 , said classification tree comprising at least one node enabling the definition of a partition on a data subset received by said node, each node being a set of partitioning rules comprising at least one rule defined by at least one condition on at least one of the attributes wherein, to generate the set of rules of each node, the method comprises the following steps, performed iteratively so long as at least one end-of-exploration criterion has not been verified: a) obtaining a new set of rules enabling a definition of a new partition on the data subset received by the node, condition or conditions that define each rule pertaining solely to one or more attributes not marked taboo; b) making a first evaluation of a quality of the new partition, by obtaining a new value of a first indicator computed with the set of data, only attributes marked target influencing said first indicator; and c) if the quality of the new partition, evaluated with the first criterion, is greater than that of the partitions evaluated during the preceding iterations, also with the first criterion, a new value of the first indicator is stored and becomes a current optimal value, and the new set of rules is stored and becomes a current optimal set of rules, so that, ultimately, the set of rules of said node is the current optimal set of rules at the time when an end-of-exploration criterion is verified.