Subspace data stream clustering with global and local weighting models

Abstract

Subspace clustering discovers clusters embedded in multiple, overlapping subspaces of high dimensional data. It has been successfully applied in many domains. Data streams are ordered and potentially infinite sequences of data points created by a typically non-stationary data generating process. Clustering this type of data requires some restrictions in time and memory. In this paper, we propose the S2G-Stream algorithm based on growing neural gas and soft subspace clustering. We introduce two types of entropy weighting for both features and blocks, and also two weighting models (local and global). Experiments on public datasets demonstrated the ability of S2G-Stream to detect relevant features and blocks and to provide the best partitioning of the data.