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

機(jī)譯：初始簇質(zhì)心的確定是否提高了K-Means聚類算法的性能？應(yīng)用研究中遺傳算法最小生成樹(shù)和分層聚類的三種混合方法的比較

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期刊名稱 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
頁(yè)碼 -1
總頁(yè)數(shù) 11
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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

機(jī)譯：初始簇質(zhì)心的確定是否提高了K-Means聚類算法的性能？應(yīng)用研究中遺傳算法，最小生成樹(shù)和分層聚類的三種混合方法的比較
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

機(jī)譯：基于遺傳算法的降維方法提高K-Means聚類性能：以醫(yī)學(xué)數(shù)據(jù)集分類為例
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

機(jī)譯：基于遺傳算法的降維方法提高K-Means聚類性能：以醫(yī)學(xué)數(shù)據(jù)集分類為例
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

機(jī)譯：利用基于遺傳算法初始化群集中心和基于糖尿病患者的熵的模糊聚類來(lái)提高K-Means聚類的性能
5. A minimum spanning tree based clustering algorithm for high throughput biological data. [D] . Pirim, Harun. 2011

機(jī)譯：用于高通量生物數(shù)據(jù)的基于最小生成樹(shù)的聚類算法。
6. Clustering performance comparison using K-means and expectation maximization algorithms [O] . Yong Gyu Jung, Min Soo Kang, Jun Heo -1

機(jī)譯：使用K均值和期望最大化算法的聚類性能比較
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

機(jī)譯：初始簇質(zhì)心的確定是否提高了K-Means聚類算法的性能？應(yīng)用研究中遺傳算法，最小生成樹(shù)和分層聚類的三種混合方法的比較
8. K-Means Re-Clustering Algorithmic Options with Quantifiable Performance Comparisons [R] . Meyer, A. W., Paglieroni, D. W., Astaneh, C. 2002

機(jī)譯：K-means通過(guò)可量化的性能比較重新聚類算法選項(xiàng)

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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

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