On nearness measures in fuzzy relational data models
International Journal of Approximate Reasoning
Imprecise information and uncertainty in information systems
ACM Transactions on Information Systems (TOIS)
On the complexity of inferring functional dependencies
Discrete Applied Mathematics - Special issue on combinatorial problems in databases
Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Efficient Discovery of Functional and Approximate Dependencies Using Partitions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Data Mining and Knowledge Discovery
Answering Imprecise Queries over Autonomous Web Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Database dependency discovery: a machine learning approach
AI Communications
Discovering functional dependencies from similarity-based fuzzy relational databases
Intelligent Data Analysis
A definition for fuzzy approximate dependencies
Fuzzy Sets and Systems
Design-level metrics estimation based on code metrics
Proceedings of the 2010 ACM Symposium on Applied Computing
ACM SIGMOD Record
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Discovery of possible relations between attribute values in a relational database (i.e., functional dependencies) is an important issue in the field of data mining and knowledge discovery. Many search techniques have been proposed to discover classical and extended functional dependencies; but even the most efficient solutions do not have an acceptable performance in the case of large relation instances. In addition, most of the proposed algorithms assume that the database is static and thus database updates require re-scanning of the entire data repeatedly. In this paper, we propose a new incremental method, AD-Miner, to discover Approximate Dependencies (ADs). The main part of our work is based on logical operations which aim to reduce the computational complexity. The method is incremental and thus avoids re-scans of database when a set of tuples is added to the relation. Our experimental results indicate that our method is more efficient than FastFDs [22] which is one of the most efficient algorithms for mining of perfect dependencies. Furthermore, we have shown that the complexity of our method is lower than major incremental methods namely partitioning and Pair-wise comparison methods. In addition, our method has the extra advantage of marking the index of the tuples that violate a dependency. This feature can be used to find the exceptional cases that are inconsistent with the rest of the data. We have implemented AD-Miner and tested it on several benchmarks and synthetic data.