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Data Stream Clustering: A Survey

机译：数据流聚类：调查

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摘要

Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.
机译：数据流挖掘是一个活跃的研究领域，最近已经出现，它可以从大量连续生成的数据中发现知识。在这种情况下，已经提出了几种数据流聚类算法来执行无监督学习。然而，数据流群集提出了一些需要解决的挑战，例如处理以在线方式到达的非平稳，无界数据。流数据的内在本质要求开发能够执行数据对象的快速和增量处理的算法，以适当地解决时间和内存限制。在本文中，我们对数据流聚类算法进行了概述，对最新算法的主要设计组件进行了全面讨论。此外，这项工作解决了数据流聚类中涉及的时间方面，并介绍了通常采用的实验方法。提供了许多参考，它们描述了数据域群集在不同域中的应用，例如网络入侵检测，传感器网络和股票市场分析。还可以提供有关软件包和数据存储库的信息，以帮助研究人员和从业人员。最后，讨论了一些可能成为未来研究主题的重要问题和未解决的问题。

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  • 来源
    《ACM Computing Surveys》 |2014年第1期|13.1-13.31|共31页
  • 作者

    JONATHAN A. SILVA; ELAINE R. FARIA; RODRIGO C. BARROS; EDUARDO R. HRUSCHKA; ANDRE C. P. L. F. DE CARVALHO; JOAO GAMA;

  • 作者单位

    Institure of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil;

    Institute of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil and School of Computer, Federal University of Uberlandia, Uberlandia, Brazil;

    Institute of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil;

    Institute of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil;

    Institute of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil;

    Laboratory of Artificial Intelligence and Decision Support (LIAAD-INESC TEC) and FEP, University of Porto, Portugal;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data stream clustering; online clustering;

    机译：数据流聚类;在线聚类;

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