多元统计方法与体育人才选择

本文应用系统聚类法从多项基本体质测试成绩中对研究对象按其素质好坏进行分类。用主成分分析方法给出研究对象素质好坏的综合评价。为选拔培养体育人才提供数量分析依据。     

模糊聚类分析的新算法

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 .     

统计在生命科学中的实践与认识(Ⅲ)二 四格表的“物理”意义和数据处理(下)

上篇涉及两个率的比较、配对计数资料的显著性和对诊断方法的评价。本篇主要涉及重复检查、危险因素分析以及时空聚集性等。     

基于概念的教育资源元素材聚类方法研究

针对教育资源元素材种类多、独立性强等特点，研究了概念聚类方法解决领域知识的分类和归并问题，并实现了概念的动态聚类算法和归并算法，分析了该算法的效率和聚类准确性．     

The discrete coagulation-fragmentation equations: Existence,uniqueness, and density conservation

The discrete coagulation-fragmentation equation describes the kinetics of cluster growth in which clusters can coagulate via binary interactions to form larger clusters or fragment to form smaller ones. These models have many applications in pure and applied science ranging from cluster formation in galaxies to the kinetics of phase transformations in binary alloys. Our results relate to existence, uniqueness, density conservation and continuous dependence and they generalise the corresponding results in [ref. 2] for the Becker-Doring equations for which the processes are restricted to clusters gaining or shedding one particle. Examples are given which illustrate the role of the assumptions on the kinetic coefficients and show the rich set of analytic phenomena supported by the general discrete coagulation-fragmentation equations.     

模糊数学方法在孢粉分析中的应用

运用模糊聚类分析的方法将已知的孢粉样本进行了分类 ,得到了动态聚类图 ,并利用方差分析的原理给出了最佳的分类 .     

Heuristic Methods for Large Centroid Clustering Problems

This article presents new heuristic methods for solving a class of hard centroid clustering problems including the p-median, the sum-of-squares clustering and the multi-source Weber problems. Centroid clustering is to partition a set of entities into a given number of subsets and to find the location of a centre for each subset in such a way that a dissimilarity measure between the entities and the centres is minimized. The first method proposed is a candidate list search that produces good solutions in a short amount of time if the number of centres in the problem is not too large. The second method is a general local optimization approach that finds very good solutions. The third method is designed for problems with a large number of centres; it decomposes the problem into subproblems that are solved independently. Numerical results show that these methods are efficient—dozens of best solutions known to problem instances of the literature have been improved—and fast, handling problem instances with more than 85,000 entities and 15,000 centres—much larger than those solved in the literature. The expected complexity of these new procedures is discussed and shown to be comparable to that of an existing method which is known to be very fast.     

A transfer algorithm for hierarchical clustering

Transfer algorithms are usually used to optimize an objective function that is defined on the set of partitions of a finite set X. In this paper we define an equivalence relation ? on the set of fuzzy equivalence relations on X and establish a bijection from the set of hierarchies on X to the set of equivalence classes with respect to ?. Thus, hierarchies can be identified with fuzzy equivalence relations and the transfer algorithm can be modified in order to optimize an objective function that is defined on the set of hierarchies on X.     

Properties of fourth-order strong mixing rates and its application to random set theory

We shall define the concept of fourth-order strong mixing rates and study their properties. Results are useful for establishing a condition of the form (*) Σ_a,b,c |cum(X_o, X_a, X_b, X_c)| < ∞ or ∫|cum(X_o, X_a, X_b, X_c)| da db dc < ∞ for dependent random variables {X_a}. As an application we shall consider an evaluation of a fourth-order strong mixing rate for a random closed set Z (in the sense of Matheron) and derive the condition (*) for {X_a}, X_a being an outcome of a local measurement upon Z. The result is also applicable to point processes which admit clustering representations.