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On discovery of extremely low-dimensional clusters using semi-supervised projected clustering

机译：使用半监督投影聚类发现极低维聚类

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Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we propose a new algorithm that can accurately identify projected clusters with relevant dimensions as few as 5% of the total number of dimensions. It makes use of a robust objective function that combines object clustering and dimension selection into a single optimization problem. The algorithm can also utilize domain knowledge in the form of labeled objects and labeled dimensions to improve its clustering accuracy. We believe this is the first semi-supervised projected clustering algorithm. Both theoretical analysis and experimental results show that by using a small amount of input knowledge, possibly covering only a portion of the underlying classes, the new algorithm can be further improved to accurately detect clusters with only 1% of the dimensions being relevant. The algorithm is also useful in getting a target set of clusters when there are multiple possible groupings of the objects.
机译：最近的研究表明，许多实际数据集中都存在维数极低的投影聚类。在过去的几年中，已经提出了许多计划的聚类算法，但是很少有人能够识别维数小于维总数的10％的聚类，这些聚类通常在某些真实的数据集（例如基因表达谱）中找到。在本文中，我们提出了一种新算法，该算法可以准确地识别相关维数仅为维维总数的5％的投影聚类。它利用了强大的目标函数，该函数将对象聚类和维度选择组合到一个优化问题中。该算法还可以利用标记对象和标记维度形式的领域知识来提高其聚类准确性。我们认为这是第一个半监督投影聚类算法。理论分析和实验结果均表明，通过使用少量输入知识（可能仅覆盖基础类的一部分），可以对新算法进行进一步改进，以仅在相关维数仅为1％的情况下准确检测聚类。当对象有多个可能的分组时，该算法在获取集群的目标集时也很有用。

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