III/EAGER: Temporal Relationships Among Clusters in Data Streams (TRACDS)

数据挖掘社区开发的最先进的数据流聚类算法没有利用事件的时间顺序，因此在结果聚类中所有的时间信息都丢失了。这很奇怪，因为数据流的一个显著特征是事件的时间顺序。在这个项目中，我们开发了一种技术来有效地将时间排序合并到聚类过程中，并证明了它在大型、高吞吐量数据流上的实用性。在数据流聚类过程中，通过动态构造一个状态表示聚类的马尔可夫链，引入时间排序。我们的方法是基于先前开发的可扩展马尔可夫模型（EMM）。该项目的结果将提供一个框架，在此框架上，可以轻松实现重要的流挖掘应用程序，如异常检测和未来事件预测。通过展示最先进的数据蒸汽聚类算法可以有效地合并时间顺序信息，该项目将对时间顺序至关重要的许多领域产生广泛的影响。例如，NOAA飓风数据和NASA卫星数据将在整个项目中使用。结果，包括开源软件将通过项目网站（http://lyle.smu.edu/ida/tracds）发布。

State-of-the-art data stream clustering algorithms developed by the data mining community do not utilize the temporal order of events and therefore in the resulting clustering all temporal information is lost. This is quite strange as one of the salient features of data streams is temporal ordering of events. In this project we develop a technique to efficiently incorporate temporal ordering into the clustering process and prove its usefulness on large, high-throughput data streams. Temporal ordering is introduced into the data stream clustering process by dynamically constructing an evolving Markov Chain where the states represent clusters. Our approach is based on the previously developed Extensible Markov Model (EMM). The results of this project will provide a framework upon which important stream mining applications such as anomaly detection and prediction of future events are easily implemented. By showing that state-of-the-art data steam clustering algorithms can incorporate temporal order information efficiently, this project will have a broad impact on many areas where temporal order is essential. As examples, NOAA Hurricane Data and NASA satellite data will be used throughout this project. Results, including open source software will be distributed via the project Web site (http://lyle.smu.edu/ida/tracds).