  Massive volumes of high-dimensional data that evolves over time is
continuously collected by contemporary information processing systems, which
brings up the problem of organizing this data into clusters, i.e. achieve the
purpose of dimensional deduction, and meanwhile learning its temporal evolution
patterns. In this paper, a framework for evolutionary subspace clustering,
referred to as LSTM-ESCM, is introduced, which aims at clustering a set of
evolving high-dimensional data points that lie in a union of low-dimensional
evolving subspaces. In order to obtain the parsimonious data representation at
each time step, we propose to exploit the so-called self-expressive trait of
the data at each time point. At the same time, LSTM networks are implemented to
extract the inherited temporal patterns behind data in an overall time frame.
An efficient algorithm has been proposed based on MATLAB. Next, experiments are
carried out on real-world datasets to demonstrate the effectiveness of our
proposed approach. And the results show that the suggested algorithm
dramatically outperforms other known similar approaches in terms of both run
time and accuracy.
