5. A method according to claim 4 , wherein the obtaining of a new value of the indicator, for a new partition, comprises the following steps: a weight equal to one is assigned to each of original attributes marked target and a weight equal to zero is assigned to each of original attributes not marked target; the set of original attributes is re-encoded as a set of attributes re-encoded Z 1 . . . Z Q such that: if an original attribute is of a discrete type and possesses N different modalities, the original attribute is re-encoded according to a full disjunctive re-encoding into N re-encoded attributes having [0; 1] as a value domain, each of the N re-encoded attributes being assigned a weight identical to that of an original attribute from which the re-encoded attribute is derived; if an original attribute is of a continuous type, the original attribute is re-encoded as a re-encoded attribute, in standardizing the re-encoded attribute's value domain at [0; 1], said re-encoded attribute being assigned a weight identical to that of an original attribute from which the re-encoded attribute is derived; the new partition is re-encoded in the space of the re-encoded attributes; and an Intra-Class Inertia of the new partition, re-encoded in the space of the re-encoded attributes, is computed.
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.