DC: Small: Stream Clustering Algorithms in Mixed Domains with Soft Two-way Semi-Supervision

DC：Small：具有软双向半监督的混合域流聚类算法

• 批准号: 0916489
• 负责人: Olfa Nasraoui
• 金额: $ 45万
• 依托单位: University of Louisville Research Foundation Inc
• 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=0916489&HistoricalAwards=false
• 关键词: DC; Small; Stream; Clustering; Algorithms

One way to form a model of massive data sets is to use clustering techniques that summarize the data by several cluster representatives. However, clustering huge data sets is a very challenging problem whose difficulty increases further when the data is dynamic. We are developing scalable and robust stream summarization methods to provide a concise summary of huge multi-dimensional data streams that keep track of each discovered cluster or component of the summary through time, and that store only milestones corresponding to the occurrence of significant changes in these cluster representatives. Moreover to handle possibly diverse data formats and different sources of data, we are using a semi-supervised framework for (i) combining diverse representations of the data, in particular where data comes from different sources, some of which may be unreliable or uncertain; and (ii) exploiting optional external concept set labels to guide the clustering of the main data set in its original domain.Our methods have tremendous impact on applications that deal with streaming data in general, and more specifically on monitoring data streams in real-life dynamic settings. For example, as more and more everyday activities move online, network and Web data has been increasing at a rapid pace that precludes standard and classical data analysis methods, and call instead for real time analysis. The same can be said about the deluge of data that is being or about to be generated by new and future sensor networks, astronomical observatories, and missions in space. Thus, new research efforts and paradigms are needed and will have a strong impact on our ability to digest and make sense of this data.

形成大规模数据集模型的一种方法是使用聚类技术，通过几个聚类代表来汇总数据。然而，聚类巨大的数据集是一个非常具有挑战性的问题，其难度进一步增加时，数据是动态的。我们正在开发可扩展的和强大的流摘要方法，以提供一个简洁的摘要，巨大的多维数据流，跟踪每个发现的集群或组件的摘要通过时间，并存储只对应于这些集群代表发生重大变化的里程碑。此外，为了处理可能不同的数据格式和不同的数据来源，我们正在使用半监督框架来（i）组合数据的不同表示，特别是当数据来自不同来源时，其中一些可能不可靠或不确定;以及（ii）利用可选的外部概念集标签来引导主数据集在其原始域中的聚类。我们的方法对一般来说，这些应用程序处理流数据，更具体地说，是在现实生活中的动态设置中监视数据流。例如，随着越来越多的日常活动移动到网上，网络和Web数据已经以快速的速度增长，这排除了标准和经典的数据分析方法，而是要求真实的时间分析。同样的道理也适用于新的和未来的传感器网络、天文观测台和太空任务正在或即将产生的大量数据。因此，需要新的研究努力和范式，这将对我们消化和理解这些数据的能力产生重大影响。