  Cluster repair methods aim to determine errors in clusters and modify them so
that each cluster consists of records representing the same entity. Current
cluster repair methodologies primarily assume duplicate-free data sources,
where each record from one source corresponds to a unique record from another.
However, real-world data often deviates from this assumption due to quality
issues. Recent approaches apply clustering methods in combination with link
categorization methods so they can be applied to data sources with duplicates.
Nevertheless, the results do not show a clear picture since the quality highly
varies depending on the configuration and dataset. In this study, we introduce
a novel approach for cluster repair that utilizes graph metrics derived from
the underlying similarity graphs. These metrics are pivotal in constructing a
classification model to distinguish between correct and incorrect edges. To
address the challenge of limited training data, we integrate an active learning
mechanism tailored to cluster-specific attributes. The evaluation shows that
the method outperforms existing cluster repair methods without distinguishing
between duplicate-free or dirty data sources. Notably, our modified active
learning strategy exhibits enhanced performance when dealing with datasets
containing duplicates, showcasing its effectiveness in such scenarios.
