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首页> 外文会议>IEEE International Conference on Data Mining Workshops >You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters
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You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters

机译：你看到一套货车 - 我看到一列火车：走向统一的本地和全球任意面向的子空间集群

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摘要

Having data with a high number of features raises the need to detect clusters which exhibit within subspaces of features a high similarity. These subspaces can be arbitrarily oriented which gave rise to arbitrarily-oriented subspace clustering (AOSC) algorithms. In the diversity of such algorithms some are specialized at detecting clusters which are global, across the entire dataset regardless of any distances, while others are tailored at detecting local clusters. Both of these views (local and global) are obtained separately by each of the algorithms. While from an algebraic point of view, none of both representations can claim to be the true one, it is vital that domain scientists are presented both views, enabling them to inspect and decide which of the representations is closest to the domain specific reality. We propose in this work a framework which is capable to detect locally dense arbitrarily oriented subspace clusters which are embedded within a global one. We also first introduce definitions of locally and globally arbitrarily oriented subspace clusters. Our experiments illustrate that this approach has no significant impact on the cluster quality nor on the runtime performance, and enables scientists to be no longer limited exclusively to either of the local or global views.
机译：具有大量特征的数据引发了检测具有高相似性的子空间内展示的集群的需要。这些子空间可以任意导向，从而产生了取向任意导向的子空间聚类（AOC）算法。在这些算法的多样性中，一些在除了任何距离内的整个数据集中的检测到全局的集群，而其他在检测到局部集群时则定制。这些视图（本地和全局）都由每个算法单独获得。虽然从代数的角度来看，但两个表示都不能声称是真实的，这是域科学家介绍了这两个视图，使他们能够检查并决定哪个表示最接近域特定现实。我们在这项工作中提出了一个框架，该框架能够检测到局部密集的任意定向的子空间集群，这些群集嵌入在全局中。我们还首先介绍本地和全球任意面向的子空间集群的定义。我们的实验表明，这种方法对群集质量没有显着影响，也没有于运行时性能，使科学家能够不再适用于本地或全球视图。

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