Dynamic Discovery of Fuzzy Functional Dependencies Using Partitions

Eecient Discovery of Functional and Approximate Dependencies Using Partitions (extended Version)

Discovery of functional and approximate functional dependencies in relational databases

This study develops the foundation for a simple, yet efficient method for uncovering functional and approximate functional dependencies in relational databases. The technique is based upon the mathematical theory of partitions defined over a relation’s row identifiers. Using a levelwise algorithm the minimal non-trivial functional dependencies can be found using computations conducted on intege...

Efficient Discovery of Functional and Approximate Dependencies Using Partitions

Discovery of functional dependencies from relations has been identified as an important database analysis technique. In this paper, we present a new approach for finding functional dependencies from large databases, based on partitioning the set of rows with respect to their attribute values. The use of partitions makes the discovery of approximate functional dependencies easy and efficient, an...

Tsallis Entropy and Conditional Tsallis Entropy of Fuzzy Partitions

The purpose of this study is to define the concepts of Tsallis entropy and conditional Tsallis entropy of fuzzy partitions and to obtain some results concerning this kind entropy. We show that the Tsallis entropy of fuzzy partitions has the subadditivity and concavity properties. We study this information measure under the refinement and zero mode subset relations. We check the chain rules for ...

Multi-Dimensional Fuzzy Transforms for Attribute Dependencies

We explore attribute dependencies in the datasets by using direct and inverse fuzzy transforms. Our algorithm optimizes the fuzzy partitions of the universe of the attributes and moreover establishes if the set of the data points is sufficiently dense with respect to the chosen partitions: two specific regression indexes measure the reliability of our model. The known “El Nino” dataset is the b...