Abstract: Record clustering is performed for a collection of records using training rules, training-rule labels, training data created from a sample of pairs of records, a pair-wise classifier, and a clustering algorithm. Record clustering is also performed for a collection of records using prediction rules, prediction-rule labels, a pair-wise classifier, and a clustering algorithm.

Abstract: A collection of clusters are selected to be used in training in an active learning workflow when using clusters to train supervised entity resolution in data sets. A collection of records is provided wherein each record in the collection has a cluster membership. A collection of record pairs is also provided, each record pair containing two distinct records from the collection of records, and each record pair having a similarity score. A collection of clusters is generated with uncertainty from the collection of records and the collection of record pairs. A subset of the collection of clusters with uncertainty is then selected using weighted sampling, wherein a function of the cluster uncertainty is used as the weight in the weighted sampling. The subset of the collection of clusters with uncertainty is the collection of clusters for training in and active learning workflow when using clusters to train supervised entity resolution in data sets.

Abstract: Methods are provided to represent proposed changes to clusterings for ease of review, as well as tools to help subject matter experts identify clusters that warrant review versus those that do not. These tools make overall assessment of proposed clustering changes and targeted curation practical at large scale. Use of these tools and method enables efficient data management operations when dealing with extreme scale, such as where entity resolution involves clusterings created from data sources involving millions of entities.

Abstract: A method is provided for producing a record clustering with estimated accuracy metrics with confidence intervals. These metrics can be used to determine whether a clustering should be accepted as the output of the system, and whether model training is necessary to meet desired clustering accuracy. A collection of test records is used in the process, wherein each test record is a member of a collection of input records.

Abstract: Methods are provided to represent proposed changes to clusterings for ease of review, as well as tools to help subject matter experts identify clusters that warrant review versus those that do not. These tools make overall assessment of proposed clustering changes and targeted curation practical at large scale. Use of these tools and method enables efficient data management operations when dealing with extreme scale, such as where entity resolution involves clusterings created from data sources involving millions of entities.

Abstract: Record clustering is performed by learning from verified clusters which are used as the source of training data in a deduplication workflow utilizing supervised machine learning.

Abstract: Given a local distance metric for geospatial features, a binning is produced that is guaranteed to label features within a given distance threshold with the same bin, while labeling a minimum number of features separated by a distance that is greater than the threshold with the same bin.