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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Incremental Affinity Propagation Clustering Based on Message Passing
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Incremental Affinity Propagation Clustering Based on Message Passing

机译：基于消息传递的增量亲和力传播聚类

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摘要

Affinity Propagation (AP) clustering has been successfully used in a lot of clustering problems. However, most of the applications deal with static data. This paper considers how to apply AP in incremental clustering problems. First, we point out the difficulties in Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve them. Correspondingly, two IAP clustering algorithms are proposed. They are IAP clustering based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment (IAPNA). Five popular labeled data sets, real world time series and a video are used to test the performance of IAPKM and IAPNA. Traditional AP clustering is also implemented to provide benchmark performance. Experimental results show that IAPKM and IAPNA can achieve comparable clustering performance with traditional AP clustering on all the data sets. Meanwhile, the time cost is dramatically reduced in IAPKM and IAPNA. Both the effectiveness and the efficiency make IAPKM and IAPNA able to be well used in incremental clustering tasks.
机译：相似性传播（AP）聚类已成功用于许多聚类问题中。但是，大多数应用程序都处理静态数据。本文考虑了如何在增量聚类问题中应用AP。首先，我们指出了增量亲和力传播（IAP）聚类中的困难，然后提出了两种解决方案。相应地，提出了两种IAP聚类算法。它们是基于K-Medoids（IAPKM）的IAP聚类和基于最近邻居分配（IAPNA）的IAP聚类。五个流行的标记数据集，现实世界时间序列和一个视频用于测试IAPKM和IAPNA的性能。还实现了传统的AP群集以提供基准性能。实验结果表明，在所有数据集上，IAPKM和IAPNA可以实现与传统AP聚类相当的聚类性能。同时，IAPKM和IAPNA的时间成本大大降低。 IAPKM和IAPNA的有效性和效率都使其能够很好地用于增量聚类任务。

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