Many practical applications of clustering involve data collected over time. In these applications, evolutionary clustering can be applied to the data to track changes in clusters with time. In this paper, we consider an evolutionary version of spectral clustering that applies a forgetting factor to past affinities between data points and aggregates them with current affinities. We propose to use an adaptive forgetting factor and provide a method to automatically choose this forgetting factor at each time step. We evaluate the performance of the proposed method through experiments on synthetic and real data and find that, with an adaptive forgetting factor, we are able to obtain improved clustering performance compared to a fixed forgetting factor.
K. S. Xu, M. Kliger, and A. O. Hero III, “Evolutionary spectral clustering with adaptive forgetting factor,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2010. (.pdf)
Please see the AFFECT MATLAB Toolbox for clustering dynamic data.