Statistical treatment of the information content of a database
Information Systems
IEEE Transactions on Software Engineering
Partial dependencies in relational databases and their realization
Discrete Applied Mathematics - Special issue on combinatorial problems in databases
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Asymptotic properties of keys and functional dependencies in random databases
Theoretical Computer Science - Special issue: database theory
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Database Management Systems
Functional and embedded dependency inference: a data mining point of view
Information Systems - Special issue on Databases: creation, management and utilization
Efficient Discovery of Functional Dependencies and Armstrong Relations
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Evaluation of Interestingness Measures for Ranking Discovered Knowledge
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Establishing the foundations of data mining
Establishing the foundations of data mining
Some analytic tools for the design of relational database systems
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
A note on approximation measures for multi-valued dependencies in relational databases
Information Processing Letters
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We consider the problem of defining an approximation measure for functional dependencies (FDs). An approximation measure for X 驴 Y is a function mapping relation instances, r, to non-negative real numbers. The number to which r is mapped, intuitively, describes the "degree" to which the dependency X 驴 Y holds in r. We develop a set of axioms for measures based on the following intuition. The degree to which X 驴 Y is approximate in r is th e degree to which r determines a function from 驴X(r) to 驴Y (r). The axioms apply to measures that depend only on frequencies (i.e. the frequency of x 驴 驴X(r) is the number of tuples containing x divided by the total number of tuples). We prove that a unique measure satisfies these axioms (up to a constant multiple), namely, the information dependency measure of [5]. We do not argue that this result implies that the only reasonable, frequency-based, measure is the information dependency measure. However, if an application designer decides to use another measure, then the designer must accept that the measure used violates one of the axioms.