QUADRATIC ASSIGNMENT AS A GENERAL DATA-ANALYSIS STRATEGY
QUADRATIC ASSIGNMENT AS A GENERAL DATA-ANALYSIS STRATEGY
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DOI：
10.1111/j.2044-8317.1976.tb00714.x
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发表时间：
1976-01-01
影响因子：
2.6
通讯作者：
SCHULTZ, J
中科院分区：
文献类型：
--
作者：
HUBERT, L;SCHULTZ, J
二次分配范式在运筹学中开发的数据分析任务的特点是使用邻近矩阵的一般方法进行了讨论。数据分析问题首先分为静态和非静态。“静态”一词意味着对一个详细的实质性假设的评估，这个假设是在没有实际数据的帮助下提出的。或者，术语“非静态”表明在所获得的邻近矩阵内搜索特定类型的关系结构，而无需事先声明特定的猜想。虽然静态类问题与经典统计学中常用的几种推理程序直接相关，但本文的主要重点是应用一般计算启发式来解决非静态问题，并使用二次分配定向来讨论行为科学，特别是心理学中的各种重要研究策略。一组广泛的数值例子，说明应用程序的搜索过程中的层次聚类，同质对象子集，线性和循环序列化的识别，和离散版本的多维缩放。
The quadratic assignment paradigm developed in operations research is discussed as a general approach to data analysis tasks characterized by the use of proximity matrices. Data analysis problems are first classified as being either static or non‐static. The term ‘static’ implies the evaluation of a detailed substantive hypothesis that is posited without the aid of the actual data. Alternatively, the term ‘non‐static’ suggests a search for a particular type of relational structure within the obtained proximity matrix and without the statement of a specific conjecture beforehand. Although the static class of problems is directly related to several inference procedures commonly used in classical statistics, the major emphasis in this paper is on applying a general computational heuristic to attack the non‐static problem and in using the quadratic assignment orientation to discuss a variety of research tactics of importance in the behavioral sciences and, particularly, in psychology. An extensive set of numerical examples is given illustrating the application of the search procedure to hierarchical clustering, the identification of homogeneous object subsets, linear and circular seriation, and a discrete version of multidimensional scaling.