Scalable Clustering of Complex Data

本研究解决了与大型复杂数据集聚类相关的三个关键问题。首先，将建立一个基于模型的聚类的统一观点，为理解和比较各种现有的复杂数据聚类算法提供理论基础。我们将系统地探讨这一观点，以开发针对特定应用的改进算法。其次，通过自适应聚类技术解决了平衡和处理增量获取的非平稳数据（如新闻源）的域约束所产生的复杂性。最后，研究了在多个聚类结果下获得单个一致解的方法。这些方法将有助于在隐私和其他约束对数据共享的严格限制下进行分布式数据挖掘。将为建议的研究领域制订基准，并提供给研究界。有关该项目的进一步信息可在该项目的网站http://www.lans.ece.utexas.edu/scalclust.html上获得。这个项目更广泛的影响还包括对高中生和新生的外展活动。将为学生设计演示模块，说明数据分析问题，使他们能够使用由这项工作产生的案例研究，并获得对聚类技术的理解。

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.