A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction

We present an algorithm for clustering high dimensional streaming data. The algorithm incorporates dimension reduction into the stream clustering framework. When a new datum arrives, the algorithm performs dimension reduction to find a local projected subspace using unsupervised LDA (Linear Discriminant Analysis)-based method. The obtained local subspace would maximally separate the nearby micro-clusters with respect to the incoming point. Then, the incoming point is assigned to a micro-cluster in the projected space, rather than in the full dimensional space. The experimental results show that the proposed algorithm outperforms its counterpart streaming clustering algorithms. Moreover, when compared with traditional clustering algorithms which require the whole data set, the proposed algorithms shows comparable clustering performances with much less computation time for large data sets.