Scalable Clustering of Complex Data

这项研究解决了与大型复杂数据集聚类有关的三个关键问题。首先，基于模型的聚类的统一视图将被开发，以形成理解和比较现有的复杂数据的聚类算法的广泛的理论基础。这一观点将被系统地探讨，以开发改进的算法，为特定的应用程序。第二，平衡和处理增量获取的非平稳数据，如新闻源，域约束所产生的复杂性，通过自适应聚类技术来解决。最后，方法获得一个单一的共识解决方案给出多个聚类结果进行了研究。这种方法将促进分布式数据挖掘的数据共享由于隐私和其他限制的严格限制。将为拟议的研究领域制定基准，并提供给研究界。关于该项目的进一步信息可在项目网站http://www.lans.ece.utexas.edu/scalclust.html上查阅。 该项目的更广泛影响还包括对高中生和新生的外联活动。演示模块，说明数据分析问题将被设计为学生，使他们能够使用从这项工作中产生的案例研究，并获得聚类技术的理解。

This research addresses three key issues pertaining to the clustering of large, complex datasets. First, a unifying view of model-based clustering will be developed to form a theoretical basis for understanding and comparing a wide range of existing clustering algorithms for complex data. This view will be systematically explored to develop improved algorithms for specific applications. Second, complexity arising from domain constraints on balancing and dealing with incrementally acquired non-stationary data, such as newsfeeds, is addressed via adaptive clustering techniques. Finally, methods for obtaining a single consensus solution given multiple clustering results are investigated. Such methods will facilitate distributed data mining under severe restrictions on data sharing due to privacy and other constraints. Benchmarks for the proposed research areas will be developed and made available to the research community. Further information about the project is available on the project web site http://www.lans.ece.utexas.edu/scalclust.html. Broader impacts of this project also include outreach activities to high school students and freshmen students. Demonstration modules that illustrate data analysis issues will be designed for students and enable them to use case studies resulting from this work and gain understanding of clustering techniques.