基于PART神经网络和相似度度量的文本集聚类

R. Krakovsky, I. Mokris
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引用次数: 2

摘要

本文利用神经网络和相似度度量对文本集进行聚类。聚类阶段是逐步传递TF矩阵的所有输入样本,并使用PART神经网络进行进一步处理。经过处理,得到投影聚类。在处理的最后阶段,我们提出的基于相似性度量的算法用于将投影聚类重定向到所需的聚类中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering of text collections based on PART neural network and similarity measure
The paper deals with clustering of the text collections by neural network and similarity measures. The clustering phase is progressively passing all input samples of TF matrix and are further processed using the PART neural network. As a result of processing, projective clusters are obtained. In the last phase of processing our propose algorithm based on similarity measure is used in order to redirect projective clusters into the required clusters.
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