  Stream clustering is a fundamental problem in many streaming data analysis
applications. Comparing to classical batch-mode clustering, there are two key
challenges in stream clustering: (i) Given that input data are changing
continuously, how to incrementally update clustering results efficiently? (ii)
Given that clusters continuously evolve with the evolution of data, how to
capture the cluster evolution activities? Unfortunately, most of existing
stream clustering algorithms can neither update the cluster result in real time
nor track the evolution of clusters.
  In this paper, we propose an stream clustering algorithm EDMStream by
exploring the Evolution of Density Mountain. The density mountain is used to
abstract the data distribution, the changes of which indicate data distribution
evolution. We track the evolution of clusters by monitoring the changes of
density mountains. We further provide efficient data structures and filtering
schemes to ensure the update of density mountains in real time, which makes
online clustering possible. The experimental results on synthetic and real
datasets show that, comparing to the state-of-the-art stream clustering
algorithms, e.g., D-Stream, DenStream, DBSTREAM and MR-Stream, our algorithm
can response to a cluster update much faster (say 7-15x faster than the best of
the competitors) and at the same time achieve comparable cluster quality.
Furthermore, EDMStream can successfully capture the cluster evolution
activities.
