"Dynamic Evolving Cluster Models Using Split-and-Merge Operations"
: Proceedings of the ICMLA 2011, 2011
Dynamic Evolving Cluster Models Using Split-and-Merge Operations
Sprache des Titels:
Proceedings of the ICMLA 2011
In this paper, we propose a new dynamic split-andmerge
concept for evolving prototype-based cluster models, i.e.
cluster partitions which are incrementally learned and extended
on-the-fly from data streams. New criteria when clusters should
be merged are based on a touching and on a homogeneity
condition between two ellipsoidal clusters, the merging itself is
conducted by using weighted averaging of cluster centers and
a convex combination of cluster spreads based on the recursive
variance update concept. The splitting criterion for an updated
cluster employs a 2-means algorithm on its sub-samples and
compares the quality of the split cluster with the original cluster
by using Bayesian information criterion; the cluster partition
with the better quality remains for the next incremental update
cycle. The results on 2-dimensional as well high-dimensional
streaming clustering data sets show that the new split-and-merge
concept is able to produce more reliable cluster partitions than
conventional evolving clustering.