量子聚类—一种新的文本分析方法

Ding Liu, Minghu Jiang, Xiaofang Yang
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引用次数: 1

摘要

本文介绍了受量子力学启发并扩展到文本分析的量子聚类。该方法对非参数密度估计进行了升级,与非参数密度估计不同的是,量子聚类不使用高斯核函数,而是使用势函数来确定聚类中心。对比实验结果证明了量子聚类方法优于传统的parzen窗口方法,在作者身份识别方面的进一步试验表明了该方法的广泛应用范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum clustering — A novel method for text analysis
The article introduces quantum clustering inspired from the quantum mechanics and extended to text analysis. This novel method upgrades the nonparametric density estimation and, different from the latter, quantum clustering constructs the potential function to determine the cluster center instead of the Gaussian kernel function. The result of a comparative experiment proves the advantage of quantum clustering over the conventional Parzen-window, and the further trial on authorship identification illustrates the wide application scope of this novel method.
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