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

形成海量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总结数据。然而，庞大数据集的聚类是一个非常具有挑战性的问题，当数据是动态的时，聚类的难度会进一步增加。我们正在开发可扩展且健壮的流摘要方法，以提供庞大的多维数据流的简明摘要，跟踪每个发现的集群或摘要组件，并且仅存储与这些集群代表中发生的重大变化相对应的里程碑。此外，为了处理可能不同的数据格式和不同的数据源，我们正在使用半监督框架来(i)结合数据的不同表示，特别是当数据来自不同的来源，其中一些可能是不可靠或不确定的；（ii）利用可选的外部概念集标签来指导主数据集在其原始域内的聚类。我们的方法对处理流数据的应用程序有巨大的影响，更具体地说，是对现实生活中动态设置中的数据流的监控。例如，随着越来越多的日常活动转移到网上，网络和Web数据以快速的速度增长，这排除了标准和经典的数据分析方法，而要求实时分析。新的和未来的传感器网络、天文观测站和太空任务正在或即将产生的海量数据也是如此。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。

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.