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

由数据挖掘社区开发的最先进的数据流聚类算法不利用事件的时间顺序，因此在所得到的聚类中丢失了所有的时间信息。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，有效地将时间排序到聚类过程中，并证明其在大型，高吞吐量的数据流的有用性。通过动态构造一个以状态表示簇的演化马尔可夫链，将时间顺序引入数据流聚类过程。我们的方法是基于以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，在该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流聚类算法可以有效地结合时序信息，该项目将对时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。包括开放源码软件在内的成果将通过项目网站（http：//lyle.smu.edu/ida/tracds）分发。

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).