共查询到16条相似文献,搜索用时 62 毫秒
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模糊聚类分析的新算法 总被引:1,自引:0,他引:1
张兴华 《数学的实践与认识》2005,35(3):138-141
提出了一种模糊聚类分析的新算法——追踪法 ,解决了以往模糊聚类分析计算量过大以及难于编程实现的问题 .该方法尤其适用于大规模数据的模糊聚类分析 ,对于模糊聚类分析的推广使用有重要意义 . 相似文献
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求解运输问题的一种新算法 总被引:6,自引:2,他引:6
本文将求解分派问题的标号算法成功地用于运输问题,并证明其中的非负处理可以省略,从而把Dijk-stra算法扩展到可能出现负边权的运输问题。与通常方法比较,这种方法具有直观、简单、计算量少、及易于推广等优点;最后证明该算法是多项式的,计算复杂性仅为o(n3)(当m≤n时)。 相似文献
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随机模糊立体运输问题的研究是为了解决现实生活中双因素不确定性问题,在遗传算法的基础上,运用可信性理论建立随机模糊运输问题的机会约束规划模型.通过算例进行VC++编程模拟计算,验证了此模型的可行性,最终提出了基于遗传算法解决随机模糊立体运输问题的模型. 相似文献
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模糊聚类分析在足球队排名中的应用 总被引:3,自引:0,他引:3
本文针对93年全国大学生数学建模竞赛B题,运用模糊聚类分析的方法,讨论了足球队比赛的排名问题,得到的结果是:T_7,T_1,T_3,T_9,T_(10),T_8,T_(11),T_(12),T_2,T_6,T_5,T_4,此排名结果合理、可信,并且对参数在一定范围内的变化有良好的稳定性。 相似文献
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图象数据分层(Image Data Hierarchy)处理技术在图象数据压缩编码、边缘检测、特征提取以及模式识别中有着广泛的应用.本文将模糊聚类方法引入图象分层中,根据图象的特点,将2×2象素块划分为包含均匀和轮廓部分的七种类型,建立了表征图象特征的相似度矩阵,修正了普通的动态聚类法,提出了λ值的自适应控制方案.最后,给出了对一幅标准“USC Girl”数字图象处理的实例以验证本方案的可行性. 相似文献
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The field of cluster analysis is primarily concerned with the partitioning of data points into different clusters so as to
optimize a certain criterion. Rapid advances in technology have made it possible to address clustering problems via optimization
theory. In this paper, we present a global optimization algorithm to solve the fuzzy clustering problem, where each data point is to be assigned to (possibly) several clusters, with a membership grade assigned
to each data point that reflects the likelihood of the data point belonging to that cluster. The fuzzy clustering problem
is formulated as a nonlinear program, for which a tight linear programming relaxation is constructed via the Reformulation-Linearization
Technique (RLT) in concert with additional valid inequalities. This construct is embedded within a specialized branch-and-bound
(B&B) algorithm to solve the problem to global optimality. Computational experience is reported using several standard data
sets from the literature as well as using synthetically generated larger problem instances. The results validate the robustness
of the proposed algorithmic procedure and exhibit its dominance over the popular fuzzy c-means algorithmic technique and the
commercial global optimizer BARON. 相似文献
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The field of cluster analysis is primarily concerned with the sorting of data points into different clusters so as to optimize
a certain criterion. Rapid advances in technology have made it possible to address clustering problems via optimization theory.
In this paper, we present a global optimization algorithm to solve the hard clustering problem, where each data point is to be assigned to exactly one cluster. The hard clustering problem is formulated
as a nonlinear program, for which a tight linear programming relaxation is constructed via the Reformulation-Linearization
Technique (RLT) in concert with additional valid inequalities that serve to defeat the inherent symmetry in the problem. This
construct is embedded within a specialized branch-and-bound algorithm to solve the problem to global optimality. Pertinent
implementation issues that can enhance the efficiency of the branch-and-bound algorithm are also discussed. Computational
experience is reported using several standard data sets found in the literature as well as using synthetically generated larger
problem instances. The results validate the robustness of the proposed algorithmic procedure and exhibit its dominance over
the popular k-means clustering technique. Finally, a heuristic procedure to obtain a good quality solution at a relative ease of computational
effort is also described. 相似文献