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.

翻译：现代数据挖掘应用要求通过跟踪聚类结果随时间的变化，在动态数据集上执行增量聚类。本文提出A-Posteriori亲和传播（APP），一种基于亲和传播（AP）的增量扩展方法，通过簇巩固与分层策略实现保真性与遗忘性。APP实现了增量聚类：i) 新到达对象被动态巩固到已有簇中，而无需对整个数据集重新执行聚类；ii) 随时间生成并维护保真的聚类结果序列，同时通过递减学习功能允许遗忘过时簇。使用四个标准标注数据集测试APP的性能，并与传统AP及基于最近邻分配的增量亲和传播（IAPNA）算法的基准聚类性能进行比较。实验结果表明，APP在保持可扩展性的同时实现了相当的聚类性能。