Functional Dependency Detection with Sequential Indexing Tables

Functional dependency detection is a very useful tool for many fields of computer science, e.g. machine learning. The presence of functional dependency in a dataset means that there is redundancy on the attribute level, so the dataset can be cropped without any loss of classification efficiency, thus making the training process faster. Many algorithms have been proposed for the detection and extraction of functional dependencies, however, they generally do not scale well with the amount of data. In this paper, a functional dependency detection method is proposed that is linear in the number of data tuples. It is based on Sequential Fuzzy Indexing Tables, a classifier structure that uses a significant amount of memory in return for a very fast operation.