Automatic optimization algorithm of clusters number based on maximum distance

机译：基于最大距离的群集数的自动优化算法

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摘要

K-means clustering algorithm clusters datasets according to the certain clustering number k. However k cannot be confirmed beforehand.A new clustering validity index was designed from the standpoint of sample geometry.Based on the index a new method for determining the optimal clustering number in K-means clustering algorithm was proposed.

机译：K-means聚类算法根据特定聚类数字k的集群数据集。然而，k无法确认。从样本几何的角度设计了新的聚类有效性索引。基于索引上的索引，提出了一种用于确定K-means聚类算法中最佳聚类数字的新方法。

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《International Conference on Intelligent Materials and Mechanical Engineering》|2014年||共4页
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作者
Hongbo Zhou; Juntao Gao;
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中图分类 TB3-532;
关键词
K-means algorithm; Euclidean distance; clustering algorithm; Cluster centers;

机译：K-means算法;欧几里德距离;聚类算法;集群中心;

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Automatic optimization algorithm of clusters number based on maximum distance

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