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Dynamic Discovery of Fuzzy Functional Dependencies Using Partitions
Abstract
Discovery of functional dependencies (FDs) from relational databases has been identified as an important database analysis technique. Various mining techniques have been proposed in recent years to deal with crisp and static data. However, few have emphasized on fuzzy data and also considered the dynamic nature that data may change all the time. In this work, the authors propose a partition-based incremental data mining algorithm to discover fuzzy functional dependencies from similarity-based fuzzy relational databases when new sets of tuples are added. Based on the concept of tuple partitions and the monotonicity of fuzzy functional dependencies, we avoid re-scanning of the database and thereby reduce computation time. An example demonstrating the proposed algorithm is given. Computational complexity of the proposed algorithm is analyzed. Comparison with pair-wise comparison-based incremental mining algorithm (Wang, Shen & Hong, 2000) is presented. It is shown that with certain space requirement, partition-based approach is more time efficient than pair-wise approach in the discovery of fuzzy functional dependencies dynamically.
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