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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

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

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期刊名称 Computational and Mathematical Methods in Medicine
作者
Saeedeh Pourahmad; Atefeh Basirat; Amir Rahimi; Marziyeh Doostfatemeh;
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年(卷),期 2020(-1),-1
年度 2020
页码 -1
总页数 11
原文格式 PDF
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1. 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
2. Genetic Algorithm Based Dimensionality Reduction for Improving Performance of K-Means Clustering: A Case Study for Categorization of Medical Dataset [J] . Asha Gowda Karegowda, Vidya T. Shama, M.A. Jayaram, International journal of soft computing . 2012,第5期

机译：基于遗传算法的降维方法提高K-Means聚类性能：以医学数据集分类为例
3. Genetic Algorithm Based Dimensionality Reduction for Improving Performance of K-Means Clustering: A Case Study for Categorization of Medical Dataset [J] . Asha Gowda Karegowda, Vidya T. Shama, M.A. Jayaram, International journal of soft computing . 2012,第5期

机译：基于遗传算法的降维方法提高K-Means聚类性能：以医学数据集分类为例
4. Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients [C] . Asha Gowda Karegowda, Vidya T., Shama, International Conference on Advances in Computing . 2013

机译：利用基于遗传算法初始化群集中心和基于糖尿病患者的熵的模糊聚类来提高K-Means聚类的性能
5. A minimum spanning tree based clustering algorithm for high throughput biological data. [D] . Pirim, Harun. 2011

机译：用于高通量生物数据的基于最小生成树的聚类算法。
6. Clustering performance comparison using K-means and expectation maximization algorithms [O] . Yong Gyu Jung, Min Soo Kang, Jun Heo -1

机译：使用K均值和期望最大化算法的聚类性能比较
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
8. K-Means Re-Clustering Algorithmic Options with Quantifiable Performance Comparisons [R] . Meyer, A. W., Paglieroni, D. W., Astaneh, C. 2002

机译：K-means通过可量化的性能比较重新聚类算法选项

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

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