# A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules

@inproceedings{Medina2009AUH, title={A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules}, author={Raoul Medina and Lhouari Nourine}, booktitle={ICFCA}, year={2009} }

Conditional Functional Dependencies (CFDs) are Functional Dependencies (FDs) that hold on a fragment relation of the original relation. In this paper, we show the hierarchy between FDs, CFDs and Association Rules (ARs): FDs are the union of CFDs while CFDs are the union of ARs. We also show the link between Approximate Functional Dependencies (AFDs) and approximate ARs. In this paper, we show that all those dependencies are indeed structurally the same and can be unified into a single hierarchy…

## 27 Citations

### Conditional Functional Dependencies: An FCA Point of View

- Computer ScienceICFCA
- 2010

A monotone function on CFDs allowing search and pruning strategies is exhibited and it is shown that transitive edges induce redundant CFDs.

### Discovering (frequent) constant conditional functional dependencies

- Computer ScienceInt. J. Data Min. Model. Manag.
- 2012

This paper introduces the first results on constant CFD inference and focuses on two types of techniques inherited from FD inference: the first one extends the notion of agree sets and the second one extendsThe notion of non-redundant sets, closure and quasi-closure.

### Characterizing approximate-matching dependencies in formal concept analysis with pattern structures

- Computer ScienceDiscret. Appl. Math.
- 2018

### Characterizing functional dependencies in formal concept analysis with pattern structures

- Computer ScienceAnnals of Mathematics and Artificial Intelligence
- 2014

This work shows how to characterize functional dependencies using the formalism of pattern structures, an extension of classical FCA to handle complex data, and shows how another class of dependencies can be characterized with that framework, namely, degenerated multivalued dependencies.

### Discovering Conditional Functional Dependencies

- Computer Science2009 IEEE 25th International Conference on Data Engineering
- 2009

This paper develops techniques for discovering CFDs from sample relations that can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery, and leverages closed-itemset mining to reduce search space.

### Explorer Discovering Conditional Functional Dependencies

- Computer Science
- 2009

This paper provides three methods for CFD discovery, based on techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns only.

### Discovering Functional Dependencies and Association Rules by Navigating in a Lattice of OLAP Views

- Computer ScienceCLA
- 2010

This work proposes a new way to display and navigate through Functional Dependencies rules, based on On-Line Analytical Processing (OLAP), presenting a set of rules as a cube, where dimensions correspond to the premises of rules.

### Revisiting Conditional Functional Dependency Discovery: Splitting the "C" from the "FD"

- Computer ScienceECML/PKDD
- 2018

This paper regards CFDs as an extension of association rules, and presents three general methodologies for (approximate) CFD discovery, each using a different way of combining pattern mining for discovering the conditions (the “C” in CFD) with FD discovery.

### Performance of Data Cleaning Techniques

- Computer Science
- 2012

To effectively identify data cleaning rules, 4 techniques for cleaning the data from sample relations are taken and found out time and space complexity of each algorithm to know which technique will be helpful in which case and display the results in the form of line and bar charts.

### Mining Constant Conditional Functional Dependencies for Improving Data Quality

- Computer Science
- 2020

Experimental results on two real-world data sets show that the data mining techniques in the area of data cleaning as effective in discovering Constant Conditional Functional Dependencies from relational databases.

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