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Subspace clustering of data streams: new algorithms and effective evaluation measures

机译：数据流子空间聚类：新算法和有效评估措施

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摘要

Nowadays, most streaming data sources are becoming high dimensional. Accordingly, subspace stream clustering, which aims at finding evolving clusters within subgroups of dimensions, has gained a significant importance. However, in spite of the rich literature of subspace and projected clustering algorithms on static data, only three stream projected algorithms are available. Additionally, existing subspace clustering evaluation measures are mainly designed for static data, and cannot reflect the quality of the evolving nature of data streams. On the other hand, available stream clustering evaluation measures care only about the errors of the full-space clustering but not the quality of subspace clustering. In this article we present a method for designing new stream subspace and projected algorithms. We propose also, to the first of our knowledge, the first subspace clustering measure that is designed for streaming data, called SubCMM: Subspace Cluster Mapping Measure. SubCMM is an effective evaluation measure for stream subspace clustering that is able to handle errors caused by emerging, moving, or splitting subspace clusters. Additionally, we propose a novel method for using available offline subspace clustering measures for data streams over the suggested new algorithms within the Subspace MOA framework.
机译：如今，大多数流数据源都已成为高维。因此，旨在寻找维度子组内不断发展的集群的子空间流聚类已经变得非常重要。但是，尽管有很多关于静态数据的子空间和投影聚类算法的文献，但是只有三种流投影算法可用。此外，现有的子空间聚类评估措施主要是针对静态数据而设计的，无法反映数据流不断发展的性质的质量。另一方面，可用的流聚类评估措施仅关注全空间聚类的错误，而不关注子空间聚类的质量。在本文中，我们提出了一种用于设计新流子空间和投影算法的方法。据我们所知，我们还提出了第一个为流数据设计的子空间聚类度量，称为SubCMM：子空间聚类映射度量。 SubCMM是针对流子空间集群的有效评估指标，它能够处理由于出现，移动或拆分子空间集群而引起的错误。此外，我们提出了一种新颖的方法，用于在Subspace MOA框架内通过建议的新算法对数据流使用可用的离线子空间聚类度量。

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