Method for partitioning a data set into frequency vectors for clustering

摘要

A method of partitioning a data set in which certain elements of the data set are first identified as robust discriminator data elements. For the other non-discriminator data elements, an embodiment of the invention counts occurrences of a predetermined relationship between each non-discriminator data element and the identified robust discriminator data elements, and maps the counted occurrences onto vectors a multi-dimensional frequency space. Finally, an embodiment forms the frequency vectors into clusters according to a distance or adjacency metric, where each cluster represents a different contextual class of meaningful attributes. The data set is thereby partitioned into an arbitrary number of clusters according to the discovered relationships between the non-discriminator data elements and the robust discriminator data elements so that all of the non-discriminator data elements located in the same cluster possess similar attributes.