文件名称:Desktop
- 所属分类:
- 人工智能/神经网络/遗传算法
- 资源属性:
- [Matlab] [源码]
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- 2016-04-13
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- 2kb
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- 问**
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传统的K-medoids聚类算法的聚类结果随初始中心点的
不同而波动,且计算复杂度较高不适宜处理大规模数据集;
快速K-medoids聚类算法通过选择合适的初始聚类中心改进
了传统K-medoids聚类算法,但是快速K-medoids聚类算法
的初始聚类中心有可能位于同一类簇。为了克服传统的K-
medoids聚类算法和快速K-medoids聚类算法的缺陷,提出
一种基于粒计算的K-medoids聚类算法。-Traditional clustering K-medoids clustering algorithm with the initial centers
Different swings, and a high degree of computational complexity inappropriate handling large data sets
Fast K-medoids clustering algorithm by selecting appropriate initial cluster centers to improve
The traditional K-medoids clustering algorithm, but fast clustering algorithm K-medoids
The initial cluster centers may be located in the same class clusters. In order to overcome the traditional K-
Medoids defect clustering algorithm and fast K-medoids clustering algorithm,
K-medoids one kind of clustering algorithm based on granular computing.
不同而波动,且计算复杂度较高不适宜处理大规模数据集;
快速K-medoids聚类算法通过选择合适的初始聚类中心改进
了传统K-medoids聚类算法,但是快速K-medoids聚类算法
的初始聚类中心有可能位于同一类簇。为了克服传统的K-
medoids聚类算法和快速K-medoids聚类算法的缺陷,提出
一种基于粒计算的K-medoids聚类算法。-Traditional clustering K-medoids clustering algorithm with the initial centers
Different swings, and a high degree of computational complexity inappropriate handling large data sets
Fast K-medoids clustering algorithm by selecting appropriate initial cluster centers to improve
The traditional K-medoids clustering algorithm, but fast clustering algorithm K-medoids
The initial cluster centers may be located in the same class clusters. In order to overcome the traditional K-
Medoids defect clustering algorithm and fast K-medoids clustering algorithm,
K-medoids one kind of clustering algorithm based on granular computing.
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lizi.m
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