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

III/EAGER：数据流中集群之间的时间关系 (TRACDS)

• 批准号: 0948893
• 负责人: Margaret Dunham
• 金额: $ 18万
• 依托单位: Southern Methodist University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0948893&HistoricalAwards=false
• 关键词: III; EAGER; Temporal; Relationships; Among

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).

由数据挖掘界开发的最新数据流聚类算法没有利用事件的时间顺序，因此在所产生的聚类中，所有的时间信息都会丢失。这非常奇怪，因为数据流的显著特征之一是事件的时间顺序。在这个项目中，我们开发了一种技术，将时间排序有效地合并到集群过程中，并证明了它在大型、高吞吐量数据流上的有效性。通过动态构造状态表示簇的演化马尔可夫链，将时序引入数据流聚类过程。我们的方法是基于先前开发的可扩展马尔可夫模型(EMM)。该项目的成果将提供一个框架，在该框架上可以容易地实施异常检测和未来事件预测等重要的流挖掘应用程序。通过展示最先进的数据流聚类算法可以有效地合并时序信息，该项目将对许多时序至关重要的领域产生广泛影响。例如，NOAA飓风数据和NASA卫星数据将在整个项目中使用。结果，包括开放源码软件将通过项目网站(http://lyle.smu.edu/ida/tracds).)分发