Incremental Affinity Propagation based on Cluster Consolidation and Stratification

TL;DR

This paper introduces APP, an incremental clustering method based on affinity propagation that maintains cluster fidelity over time while efficiently handling dynamic datasets.

Contribution

The paper presents APP, a novel incremental affinity propagation algorithm that consolidates and stratifies clusters to improve scalability and adapt to evolving data.

Findings

APP achieves comparable clustering performance to traditional methods.

APP maintains cluster fidelity over time with incremental updates.

Experimental results demonstrate APP's scalability and effectiveness.

Abstract

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.