Reuben Ndindi

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Correlation clustering is to partition a set of objects into clusters such that the number of false positives and negatives is minimised. In this paper, we combine correlation clustering and user interaction. More specifically, we allow the user to control the quality of the clustering by providing error bounds on the number of false positives and(More)
Correlation clustering is to partition a set of objects into clusters such that the number of false positives and negatives is minimized. In this paper, we combine correlation clustering and user interaction. More specifically, we allow the user to control the quality of the clustering by providing error bounds on the number of false positives and(More)
Conditional functional dependencies (CFDs) have proved effective in improving the quality of data. Not only are CFDs useful in detecting inconsistencies in data, they are also used to resolve inconsistencies in a database (or to repair the data). Most existing CFD-based repairing algorithms perform a variation of the so-called chase procedure, a standard(More)
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