Clustering and cluster tracking of categorical data

Abstract

An approach for clustering large sets of categorical data involves iteratively ordering the data points, partitioning the data into blocks based on the ordering, and clustering the data points within each block, where different iterations use different orderings and, thus, different partitionings. In some embodiments, the data points are represented by multi-dimensional categorical vectors, and the orderings are based on permutations of the categorical dimensions. The iterative clustering may be repeated for multiple successive time windows to track the clusters. Various applications of the disclosed clustering approach, including for cyber security, are also described.