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Comparison of Prim and Kruskal on Shanghai and Shenzhen 300 Index hierarchical structure tree

机译:上海和深圳300指数等级结构树的Prim和Kruskal比较

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This study's objective is to assess the performance of the common Prim and the Kruskal of the minimum spanning tree in building up super metric space. We proposed the use of complexity analysis and experimental methods to assess these two methods. After the analysis of the sample data of Shanghai and Shenzhen 300 index daily from the second half of 2005 to the second half of 2007, The results showed that when the number of shares is less than 100, judging from the space complexity, Kruskal algorithm is relatively superior to Prim algorithm; however, when the number of shares is greater than 100, from the time complexity's aspect, Prim algorithm is more superior. This study's conclusion indicates that matrix of Shanghai and Shenzhen 300 stock the inter-continental distance formed by the edge map more, so Prim better than Kruskal.
机译:本研究的目标是评估共同的Prim和kruskal在构建超级公制空间方面的最小生成树。我们提出使用复杂性分析和实验方法来评估这两种方法。从2005年下半年到2007年下半年的上海和深圳300指数的分析后,结果表明,当股票数量小于100时,从空间复杂性判断,克鲁斯算法是相对优于原始算法;但是,当股票数量大于100时,从时间复杂性方面,PRIM算法更优越。本研究的结论表明上海和深圳的矩阵300库存由边缘地图形成的大陆距离更多,比kruskal更好。

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