Incremental Affinity Propagation based on Cluster Consolidation and Stratification
This work addresses incremental clustering for data mining applications, but it is incremental as it extends an existing method with consolidation and stratification.
The paper tackles the problem of incremental clustering for dynamic datasets by proposing A-Posteriori affinity Propagation (APP), which dynamically consolidates new objects into existing clusters without full re-clustering and maintains results over time. Experimental results on four datasets show that APP achieves comparable clustering performance to benchmarks while enforcing scalability.
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.