Incremental Affinity Propagation based on Cluster Consolidation and
  Stratification
Silvana Castano Alfio Ferrara Stefano Montanelli Francesco Periti
Modern data mining applications require to perform incremental clustering
over dynamic datasets by tracing temporal changes over the resulting clusters.
In this paper, we propose A-Posteriori affinity Propagation (APP), an
incremental extension of Affinity Propagation (AP) based on cluster
consolidation and cluster stratification to achieve faithfulness and
forgetfulness. APP enforces incremental clustering where i) new arriving
objects are dynamically consolidated into previous clusters without the need to
re-execute clustering over the entire dataset of objects, and ii) a faithful
sequence of clustering results is produced and maintained over time, while
allowing to forget obsolete clusters with decremental learning functionalities.
Four popular labeled datasets are used to test the performance of APP with
respect to benchmark clustering performances obtained by conventional AP and
Incremental Affinity Propagation based on Nearest neighbor Assignment (IAPNA)
algorithms. Experimental results show that APP achieves comparable clustering
performance while enforcing scalability at the same time.