A clustering algorithm for multivariate data streams with correlated components

TL;DR

This paper introduces a novel clustering algorithm for multivariate data streams that accounts for correlated components and varying covariance matrices, using an optimal double shrinkage method for robust covariance estimation.

Contribution

The proposed algorithm extends existing streaming clustering methods by handling correlated data components and estimating covariance matrices with a double shrinkage approach.

Findings

Effective in processing correlated multivariate data streams.

Accurately estimates the number of clusters from data.

Provides positive definite covariance estimates even with limited data.

Abstract

Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with data arriving in streams, must be processed. Some algorithms to extend the popular K-means method to the analysis of streaming data are present in literature since 1998 (Bradley et al. in Scaling clustering algorithms to large databases. In: KDD. p. 9-15, 1998; O'Callaghan et al. in Streaming-data algorithms for high-quality clustering. In: Proceedings of IEEE international conference on data engineering. p. 685, 2001), based on the memorization and recursive update of a small number of summary statistics, but they either don't take into account the specific variability of the clusters, or assume that the random vectors which are processed and grouped have uncorrelated components. Unfortunately this is not the case in many practical situations. We here propose a new algorithm to process data streams, with data having correlated components and coming from clusters with different covariance matrices. Such covariance matrices are estimated via an optimal double shrinkage method, which provides positive definite estimates even in presence of a few data points, or of data having components with small variance. This is needed to invert the matrices and compute the Mahalanobis distances that we use for the data assignment to the clusters. We also estimate the total number of clusters from the data.