Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A relational model of data for large shared data banks
Communications of the ACM
Database Management Systems
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ECML '95 Proceedings of the 8th European Conference on Machine Learning
Approximate Dependency Inference from Relations
ICDT '92 Proceedings of the 4th International Conference on Database Theory
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
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ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
On approximation measures for functional dependencies
Information Systems - Special issue: ADBIS 2002: Advances in databases and information systems
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Information-theoretic tools for mining database structure from large data sets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
SQL-based discovery of exact and approximate functional dependencies
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Approximate functional dependency discovery and one-dimensional selectivity estimation
Approximate functional dependency discovery and one-dimensional selectivity estimation
Query processing over incomplete autonomous databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An integrated, generic approach to pattern mining: data mining template library
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Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
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The task of searching a powerset lattice is part of many problem domains. In Approximate Functional Dependencies (AFDs) mining the algorithm used for searching lattices has been bottom-up breadth first search (BU-BFS). This paper introduces as part of the MoLS framework a method for managing and using inferred information about the status of rules in the lattice. BU-BFS however has limited ability to use inferred information. This paper instead explores the ability of depth first search (DFS) algorithms to use inferred information. The focus on algorithms is motivated by the idea that improved performance from an algorithm is more likely to persist as users make customizations or use different approximation measures. Algorithms are experimentally evaluated and performance is judged based on the portion of the search space for which the approximation measure has been calculated. This presents a machine independent evaluation where differences in performance can be directly attributed to the algorithm.