Abstract: Records linking is provided. Two records are selected from a plurality of records corresponding to a customer for pair-wise record comparison. It is determined whether the two records are included in different entities. A local auto-link-threshold value of the different entities is identified in response to determining that the two records are included in different entities. An attribute comparison is performed between the two records. A comparison score is generated based on the attribute comparison between the two records. It is determined whether the comparison score is greater than the local auto-link-threshold value of the different entities. The two records are linked in response to determining that the comparison score is greater than the local auto-link-threshold value of the different entities.

Abstract: A multilevel clustered data set for multidimensional vectors is created by defining a plurality of clusters based on each of the signed dimensions of the vectors, each dimension functioning as an axis. Vectors are assigned to each cluster by measuring cosine similarity between a vector and each axis. Sub-clusters are defined as ranges of cosine similarity values within a cluster, and each vector is assigned into the appropriate range based on their cosine similarity value with the axis of the cluster. Searching for a matching vector to a new vector is efficiently achieved in near-constant time by measuring cosine similarity for the new vector with each axis to identify the closest cluster, reusing the cosine similarity of the new vector and axis to determine which sub-cluster corresponds to the appropriate range of values, and then comparing each vector within the sub-cluster until a match is found or ruled out.

Abstract: A multilevel clustered data set for multidimensional vectors is created by defining a plurality of clusters based on each of the signed dimensions of the vectors, each dimension functioning as an axis. Vectors are assigned to each cluster by measuring cosine similarity between a vector and each axis. Sub-clusters are defined as ranges of cosine similarity values within a cluster, and each vector is assigned into the appropriate range based on their cosine similarity value with the axis of the cluster. Searching for a matching vector to a new vector is efficiently achieved in near-constant time by measuring cosine similarity for the new vector with each axis to identify the closest cluster, reusing the cosine similarity of the new vector and axis to determine which sub-cluster corresponds to the appropriate range of values, and then comparing each vector within the sub-cluster until a match is found or ruled out.