基于聚类质量的聚类数实验估计
Experimental Estimation of Number of Clusters Based on Cluster Quality
摘要：
文本聚类是一种文本挖掘技术，它将给定的文本文档集划分为重要的聚类。它用于将大量的文本文档组织成组织良好的形式。在大多数聚类算法中，聚类数必须事先指定，这是这些算法的一个缺点。本文的目的是实验展示如何确定基于聚类质量的聚类数。由于分区聚类算法非常适合于对大型文档数据集进行聚类，因此我们的分析仅限于分区聚类算法。
英文摘要：
Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
