Amperity, Inc.

Jeffrey Allen Stokes

Dan Suciu

Yan Yan

Stephen Meyles

Carlos Minoru Sakoda

Graeme Andrew Kyle Roche

The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. In this respect, a higher confidence may lead to smaller sized clusters while a lower confidence may lead to larger sized clusters. Ordinal classification may be used to generate hierarchical clusters. In some embodiments, hierarchical clustering with conflict resolution is used to resolve user-defined hard conflicts in each tier of the clustering results.

Multi-level conflict-free entity clusters

Multi-level conflict-free entity clusterings

The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. A hierarchical cluster ID is generated for respective data records. The hierarchical cluster ID may be made up of a series of values, wherein each value reflects a tier within the hierarchical clustering scheme. A user may enter a partial hierarchical cluster ID to select clusters associated with a lower confidence. Thus, in some embodiments, the hierarchical cluster ID is variable in length in a manner that corresponds to the tiers in the hierarchical clustering scheme.

Clustering of data records with hierarchical cluster IDs

Growing community of inventors

Seattle, WA, United States of America

Jeffrey Allen Stokes