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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering
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Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering

机译：具有时变结构的子空间和投影聚类的维选择性自组织映射

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摘要

Subspace clustering is the task of identifying clusters in subspaces of the input dimensions of a given dataset. Noisy data in certain attributes cause difficulties for traditional clustering algorithms, because the high discrepancies within them can make objects appear too different to be grouped in the same cluster. This requires methods specially designed for subspace clustering. This paper presents our second approach to subspace and projected clustering based on self-organizing maps (SOMs), which is a local adaptive receptive field dimension selective SOM. By introducing a time-variant topology, our method is an improvement in terms of clustering quality, computational cost, and parameterization. This enables the method to identify the correct number of clusters and their respective relevant dimensions, and thus it presents nearly perfect results in synthetic datasets and surpasses our previous method in most of the real-world datasets considered.
机译：子空间聚类是识别给定数据集输入维度的子空间中的聚类的任务。某些属性中的嘈杂数据会给传统的聚类算法带来困难，因为它们之间的高差异可能会使对象看起来差异太大，无法在同一聚类中进行分组。这需要专门为子空间聚类设计的方法。本文介绍了我们基于自组织映射（SOM）的子空间和投影聚类的第二种方法，它是一种局部自适应接收场维选择性SOM。通过引入时变拓扑，我们的方法在聚类质量，计算成本和参数化方面进行了改进。这使该方法能够识别正确数量的聚类及其各自的相关维度，从而在合成数据集中显示近乎完美的结果，并在考虑的大多数现实数据集中超越了我们先前的方法。

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