Deriving cluster analytic distance functions from Gaussian mixture models
Deriving cluster analytic distance functions from Gaussian mixture models
复制标题
从高斯混合模型导出聚类分析距离函数
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Michael E. Tipping
中科院分区：
文献类型：
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作者：
Michael E. Tipping
在非平凡维度的数据集中可靠地检测聚类是出了名的困难。聚类算法通常由定义在成对示例上的一些距离函数（通常是欧几里得函数）驱动，它隐式地对待聚类内部和聚类之间的距离。本文从先验估计高斯混合模型出发，提出了一种更有效的距离度量方法。给出的例子说明了所提出的方法如何有效地去强调簇内结构，从而隐式地放大高数据密度区域之间的分离。
The reliable detection of clusters in datasets of non-trivial dimensionality is notoriously difficult. Clustering algorithms are generally driven by some distance function (usually Euclidean) defined over pairs of examples, which implicitly treats distances within and between clusters alike. In this paper, a more effective distance measure is proposed, derived from an a priori estimated Gaussian mixture model. Examples are given to illustrate how the proposed approach can effectively de-emphasise within-cluster structure, and thus implicitly magnify the separation between regions of high data density.