用于聚类算法性能度量的类分配算法

Jie Zhang, Xingsi Xue, Yuping Wang
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引用次数: 2

摘要

为了衡量聚类算法的性能或有效性,定义了几个评价值,如成功率、成功次数和完全成功率。为了保证每个聚类至少包含一个向量数据,并最大化几个建议的评价值,设计了两种类分配算法。为了验证它们的性能,我们将它们应用到k-means聚类算法中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Class Assignment Algorithms for Performance Measure of Clustering Algorithms
To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means clustering algorithms.
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