Mining approximate temporal functional dependencies with pure temporal grouping in clinical databases

Functional dependencies (FDs) typically represent associations over facts stored by a database, such as “patients with the same symptom get the same therapy.” In more recent years, some extensions have been introduced to represent both temporal constraints (temporal functional dependencies – TFDs), as “for any given month, patients with the same symptom must have the same therapy, but their therapy may change from one month to the next one,” and approximate properties (approximate functional dependencies – AFDs), as “patients with the same symptom generally have the same therapy.” An AFD holds most of the facts stored by the database, enabling some data to deviate from the defined property: the percentage of data which violate the given property is user-defined.