  Density-based cluster mining is known to serve a broad range of applications
ranging from stock trade analysis to moving object monitoring. Although methods
for efficient extraction of density-based clusters have been studied in the
literature, the problem of summarizing and matching of such clusters with
arbitrary shapes and complex cluster structures remains unsolved. Therefore,
the goal of our work is to extend the state-of-art of density-based cluster
mining in streams from cluster extraction only to now also support analysis and
management of the extracted clusters. Our work solves three major technical
challenges. First, we propose a novel multi-resolution cluster summarization
method, called Skeletal Grid Summarization (SGS), which captures the key
features of density-based clusters, covering both their external shape and
internal cluster structures. Second, in order to summarize the extracted
clusters in real-time, we present an integrated computation strategy C-SGS,
which piggybacks the generation of cluster summarizations within the online
clustering process. Lastly, we design a mechanism to efficiently execute
cluster matching queries, which identify similar clusters for given cluster of
analyst's interest from clusters extracted earlier in the stream history. Our
experimental study using real streaming data shows the clear superiority of our
proposed methods in both efficiency and effectiveness for cluster summarization
and cluster matching queries to other potential alternatives.
