A genetic clustering algorithm for searching the non-spherical-shape clusters

机译：寻找非球形聚类的遗传聚类算法

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The K-means algorithm is a well-known method for searching the clustering. However, the K-means algorithm is suitable to find the clustering that contains compact spherical clusters. If the shape of clusters is not spherical, the K-means algorithm is failure to find the clustering result. Therefore, in this study, the genetic clustering algorithm is proposed to find the clustering whether the shape of clusters is spherical or not. Also, the genetic clustering algorithm can automatically find the number of clusters in the data set. Thus, the users need not to pre-define the number of clusters in the data set. Experimental results show our proposed genetic clustering algorithm achieves better performance than the traditional clustering algorithms.

机译：K-means算法是搜索聚类的一种众所周知的方法。但是，K-means算法适合查找包含紧凑球形簇的簇。如果聚类的形状不是球形，则K-means算法将无法找到聚类结果。因此，在本研究中，提出了一种遗传聚类算法来寻找聚类的形状是否为球形的聚类。同样，遗传聚类算法可以自动找到数据集中的聚类数量。因此，用户无需预先定义数据集中的簇数。实验结果表明，本文提出的遗传聚类算法比传统聚类算法具有更好的性能。

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来源
《Sixth International Conference on Quality Control by Artificial Vision May 19-22, 2003 Gatlinburg, Tennessee, USA》|2003年|p.546-550|共5页
会议地点 Gatlinburg TN(US)
作者
Shiueng B. Yang; Yi L. Lee;
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作者单位

Institute of Applied Information, Leader University No. 188 Sec. 5 An-chung Rd., Tainan City, Taiwan;

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会议组织
原文格式 PDF
正文语种 eng
中图分类工业经济;
关键词
clustering; genetic algorithm;

机译：聚类;遗传算法;

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专利

1. A genetic clustering algorithm for data with non-spherical-shape clusters [J] . Tseng LY., Yang SB. Pattern Recognition: The Journal of the Pattern Recognition Society . 2000,第7期

机译：非球形簇数据的遗传聚类算法
2. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study [J] . Saeedeh Pourahmad, Atefeh Basirat, Amir Rahimi, Computational and mathematical methods in medicine . 2020,第1期

机译：初始簇质心的确定是否提高了K-Means聚类算法的性能？应用研究中遗传算法，最小生成树和分层聚类的三种混合方法的比较
3. CLUSTERING AND SEARCHING TECHNIQUE FOR SELECTION HORTICULTURAL USING SELF ORGANIZING MAPS AND GENETIC ALGORITHM [J] . ASTI DWI IRFIANTI, RETANTYO WARDOYO, SRI HARTATI, Journal of Theoretical and Applied Information Technology . 2016,第2期

机译：自组织映射和遗传算法的园艺选择聚类搜索技术
4. A genetic clustering algorithm for searching the non-spherical-shape clusters [C] . Shiueng B. Yang, Yi L. Lee, SPIE-The International Society for Optical Engineering International Conference on Quality Control by Artificial Vision . 2003

机译：一种用于搜索非球形簇的基因聚类算法
5. Improving the standard ant clustering algorithm using genetic algorithms. [D] . AlFraihi, Mohammed Hamad. 2014

机译：使用遗传算法改进标准蚂蚁聚类算法。
6. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm Minimum Spanning Tree and Hierarchical Clustering in an Applied Study [O] . Saeedeh Pourahmad, Atefeh Basirat, Amir Rahimi, 2020

机译：初始簇质心的确定是否提高了K-Means聚类算法的性能？应用研究中遗传算法最小生成树和分层聚类的三种混合方法的比较
7. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study [O] . Saeedeh Pourahmad, Atefeh Basirat, Amir Rahimi, 2020

机译：初始簇质心的确定是否提高了K-Means聚类算法的性能？应用研究中遗传算法，最小生成树和分层聚类的三种混合方法的比较

A genetic clustering algorithm for searching the non-spherical-shape clusters

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