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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Evolutionary Clustering via Message Passing
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Evolutionary Clustering via Message Passing

机译：通过消息传递进化聚类

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摘要

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this article we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.
机译：我们经常感兴趣的聚类随时间演进的对象，并确定到聚类问题解决方案，为每个时间步长。进化聚类提供了洞察簇演变和集群成员的时间变化，同时使性能优于通过独立地聚类在不同时间点收集的数据来实现的。在本文中，我们介绍进化亲和力传播（EAP），进化聚类算法组数据点通过在因子图交换消息。 EAP促进溶液来通过链接在与相邻的数据的快照相关联的因子图的节点，并介绍了共识节点，以使簇跟踪和簇的出生和死亡的识别聚类时间演变的数据的时间平滑度。与需要额外的处理来近似簇的数目或跨越时间匹配它们现有进化聚类方法，EAP确定集群的数量和自动跟踪它们。与模拟和实验数据的现有方法的比较表明了该EAP算法的有效性。

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