可扩展的双聚类——大数据探索的未来？
Scalable biclustering - the future of big data exploration?
机构信息
Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Department of Automatics and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, Kraków 30-059, Poland.
出版信息
Gigascience. 2019 Jul 1;8(7). doi: 10.1093/gigascience/giz078.
Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.
分群分析是一种在数据中发现局部相似性的技术。多年来，由于方法的复杂性和并行化问题，其应用一直受到限制，无法处理大数据问题。随着新型可扩展方法的发展，分群分析终于开始缩小这一差距。本文讨论了分群分析的注意事项，并介绍了其当前面临的挑战和从业者的指导原则。我们还试图解释为什么分群分析可能很快成为大数据分析的标准之一。
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