Maximum Clusterability Divisive Clustering
Maximum Clusterability Divisive Clustering
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DOI：
10.1109/ssci.2015.116
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
David P. Hofmeyr;N. Pavlidis
中科院分区：
文献类型：
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作者：
David P. Hofmeyr;N. Pavlidis
聚类能力的概念通常用于确定一组数据中的聚类结构有多强，以及评估聚类模型的质量。然而，在多变量应用中，数据集的聚类能力可能被不相关或噪声特征所掩盖。我们研究的问题，找到低维投影，最大限度地提高聚类能力的数据集。特别是，我们寻求低维表示的数据，最大限度地提高质量的二进制分区。我们使用这种二分区递归生成高质量的聚类模型。我们说明了标准的降维和聚类技术的改进，并评估我们的方法在实验中对真实的和模拟数据集。
The notion of cluster ability is often used to determine how strong the cluster structure within a set of data is, as well as to assess the quality of a clustering model. In multivariate applications, however, the cluster ability of a data set can be obscured by irrelevant or noisy features. We study the problem of finding low dimensional projections which maximise the cluster ability of a data set. In particular, we seek low dimensional representations of the data which maximise the quality of a binary partition. We use this bi-partitioning recursively to generate high quality clustering models. We illustrate the improvement over standard dimension reduction and clustering techniques, and evaluate our method in experiments on real and simulated data sets.