Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA

P. Kranen, Hardy Kremer, Timm Jansen, T. Seidl, A. Bifet, G. Holmes, B. Pfahringer
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引用次数: 26

Abstract

In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good they reflect the data, can be assessed by evaluation measures. A multitude of stream clustering algorithms and evaluation measures for clusterings were introduced in the literature, however, until now there is no general tool for a direct comparison of the different algorithms or the evaluation measures. In our demo, we present a novel experimental framework for both tasks. It offers the means for extensive evaluation and visualization and is an extension of the Massive Online Analysis (MOA) software environment released under the GNU GPL License.
演化数据流的聚类性能:MOA中的评估算法和评估方法
在今天的应用程序中,不断发展的数据流无处不在。引入了流聚类算法,从这些流中实时获取有用的知识。所获得的聚类的质量,即它们对数据的反映程度,可以通过评价度量来评估。文献中介绍了大量的流聚类算法和聚类的评价方法,但是到目前为止,还没有一个通用的工具可以直接比较不同的算法或评价方法。在我们的演示中,我们为这两个任务提出了一个新的实验框架。它提供了广泛的评估和可视化的手段,是在GNU GPL许可下发布的大规模在线分析(MOA)软件环境的扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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