Performance of Data Cleaning Techniques

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Provided by: International Journal on Computer Science and Technology (IJCST)
Topic: Data Management
Format: PDF
Conditional Functional Dependencies (CFDs) are an extension of Functional Dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, the authors take 4 techniques for cleaning the data from sample relations. CFD-Miner, is based on techniques for mining closed item sets, and is used to detect constant CFDs, namely, CFDs with constant patterns only. It provides a heuristic efficient algorithm for discovering patterns from a fixed FD. It leverages closed-item set mining to reduce search space.
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