Clustering and cluster tracking of categorical data

A computer system for processing categorical data items, the system comprising:

• at least one hardware computer processor;
• and
• at least one computer-readable medium storing instructions which, when executed by the at least one computer processor, cause the at least one computer processor to perform operations comprising:
• representing the data items as initial data points represented by categorical vectors comprising respective ordered sets of vector elements;
• initializing a cluster set of data points with the initial data points, the data points in the cluster set representing cluster centers and the initial data points serving as initial cluster centers of singleton clusters;
• iteratively clustering the data points in the cluster set in two or more iterations, the iterations comprising:
• creating an ordering of the data points in the cluster set based on a permutation of the vector elements within the vectors,
• partitioning the data points in the cluster set into at least two blocks based on the ordering,
• clustering the data points within the at least two blocks, and,
• for at least one cluster created as a result of the clustering, replacing, in the cluster set of data points, individual data points within the cluster by a new cluster center determined from the individual data points within the cluster, the new cluster center being represented by a categorical vector,
• wherein the permutations differ between different iterations;
• outputting one or more final clusters of the data items based on the iterative clustering;
• and
• for at least one of the one or more final clusters, performing a common action on the data items within the final cluster.

A non-transitory machine-readable medium storing instructions which, when executed by at least one computer processor, cause the at least one computer processor to perform operations comprising:

• representing the data items as initial data points represented by fixed-length categorical vectors comprising respective ordered sets of vector elements;
• initializing a cluster set of data points with the initial data points, the data points in the cluster set representing cluster centers and the initial data points serving as initial cluster centers of singleton clusters;
• iteratively clustering the data points in the cluster set in two or more iterations, the iterations comprising:
• creating an ordering of the data points in the cluster set based on a permutation of the vector elements within the vectors,
• partitioning the data points in the cluster set into at least two blocks based on the ordering,
• clustering the data points within the at least two blocks, and,
• for at least one cluster created as a result of the clustering, replacing, in the cluster set of data points, individual data points within the cluster by a new cluster center determined from the individual data points within the cluster, the new cluster center being represented by a fixed-length categorical vector,
• wherein the permutations differ between different iterations;
• outputting one or more final clusters of the data items based on the iterative clustering;
• and
• for at least one of the one or more final clusters, performing a common action on the data items within the final cluster.