31. A method for using a computer for retrieving association rules from databases, said databases being structured or unstructured, said databases being homogeneous or heterogeneous wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items in said databases being classified under one or more concepts, said items and their classification being called an extension, an association rule defining a probabilistic correlation relationship between the antecedent, said antecedent being defined by one concept in the taxonomy or by a boolean combination of concepts in the taxonomy, and the consequent, said consequent being defined by one concept in the taxonomy or by a boolean combination of concepts in the taxonomy, said method comprising, given an initial current subset of interest: using a computer for providing a reduced taxonomy for the subset of interest; using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations, the entire Boolean combination of concepts that defines the refined subset of interest being called the conceptual focus; and using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein: said initial subset of interest includes all the items in the extension of the dynamic taxonomy, or a subset of them; said reduced taxonomy being derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified; said step of pruning of concepts includes eliminating from the taxonomy the concepts under which no item in the selected subset of interest is classified, or preventing said concepts from being selected in order to specify interest sets; said intension is organized as a hierarchy of concepts or as a directed acyclic graph of concepts, thereby allowing a concept to have multiple fathers; for each concept in said reduced taxonomy, two association rules exist, the first association rule having the conceptual focus as the antecedent of said first association rule and having said concept in the reduced taxonomy as the consequent of said first association rule, the second association rule having said concept in the reduced taxonomy as the antecedent of said second association rule and having the conceptual focus as the consequent of said second association rule; in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of confidence is provided, said measure of confidence being computed as the ratio between the number of items in the intersection of the antecedent and consequent of said association rule over the number of items in the antecedent of said association rule, or said measure is not provided; in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of support is provided, said support being expressed as the number of items in the intersection of the antecedent and consequent of said association rule over the total number of items, or said measure is not provided; in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension; and in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of the statistical significance of how the subordinate probability of the consequent of said association rule with respect to the antecedent of said association rule deviates from independence of said consequent and antecedent of said association rule, is provided or said measure is not provided.
31. A method for using a computer for retrieving association rules from databases, said databases being structured or unstructured, said databases being homogeneous or heterogeneous wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items in said databases being classified under one or more concepts, said items and their classification being called an extension, an association rule defining a probabilistic correlation relationship between the antecedent, said antecedent being defined by one concept in the taxonomy or by a boolean combination of concepts in the taxonomy, and the consequent, said consequent being defined by one concept in the taxonomy or by a boolean combination of concepts in the taxonomy, said method comprising, given an initial current subset of interest: using a computer for providing a reduced taxonomy for the subset of interest; using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations, the entire Boolean combination of concepts that defines the refined subset of interest being called the conceptual focus; and using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein: said initial subset of interest includes all the items in the extension of the dynamic taxonomy, or a subset of them; said reduced taxonomy being derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified; said step of pruning of concepts includes eliminating from the taxonomy the concepts under which no item in the selected subset of interest is classified, or preventing said concepts from being selected in order to specify interest sets; said intension is organized as a hierarchy of concepts or as a directed acyclic graph of concepts, thereby allowing a concept to have multiple fathers; for each concept in said reduced taxonomy, two association rules exist, the first association rule having the conceptual focus as the antecedent of said first association rule and having said concept in the reduced taxonomy as the consequent of said first association rule, the second association rule having said concept in the reduced taxonomy as the antecedent of said second association rule and having the conceptual focus as the consequent of said second association rule; in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of confidence is provided, said measure of confidence being computed as the ratio between the number of items in the intersection of the antecedent and consequent of said association rule over the number of items in the antecedent of said association rule, or said measure is not provided; in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of support is provided, said support being expressed as the number of items in the intersection of the antecedent and consequent of said association rule over the total number of items, or said measure is not provided; in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension; and in said step of providing a reduced taxonomy, for an association rule in the reduced taxonomy, a measure of the statistical significance of how the subordinate probability of the consequent of said association rule with respect to the antecedent of said association rule deviates from independence of said consequent and antecedent of said association rule, is provided or said measure is not provided. 36. The method of claim 31 , said method applied to event log analysis wherein: said intension comprises a set of features describing events, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature; said items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature.