Topic
Combinatorial data analysis
About: Combinatorial data analysis is a research topic. Over the lifetime, 45 publications have been published within this topic receiving 2817 citations.
Papers
TL;DR: 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 in this article, and an extensive set of numerical examples are 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.
Abstract: 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.
910 citations
TL;DR: This paper presents some of the most important QAP formulations and classify them according to their mathematical sources and gives a detailed discussion of the progress made in both exact and heuristic solution methods, including those formulated according to metaheuristic strategies.
772 citations
Book•
29 Sep 1986
TL;DR: In this paper, the authors present a monograph that integrates many classes of permutation techniques in a highly cohesive manner, which is interesting because it avoids the all-too-common practice involving the assumption of totally unjustified distribution (e.g., a univariate or multivariate normal distribution) in applications of statistical methods that influences (perhaps overwhelms) any inference resulting from such methods.
Abstract: This monograph is extremely interesting because it integrates many classes of permutation techniques in a highly cohesive manner. Being confined to permutation methods, the techniques are strictly data dependent (i.e., all inferences depend only on the permutation structure associated with the available data). As a consequence, this monograph avoids the all-too-common practice involving the assumption of a totally unjustified distribution (e.g., a univariate or multivariate normal distribution) in applications of statistical methods that influences (perhaps overwhelms) any inference resulting from such methods. All the methods considered in this monograph depend on indices of merit that are called objective functions. A well-known example of an objective function is the Pearson correlation coefficient. In this perspective, the objective functions considered in this monograph are nothing more than univariate or multivariate variations and/or extensions of the Pearson correlation coefficient. The intuitive notion of proximity ("closeness" between objects) is a major consideration in the choice of an objective function. Because permutation methods involve random assignments, the statistical approaches of this monograph are termed assignment models. First, second, and higher order assignment models are considered. A general rth-order assignment model is based on an objective function given by
261 citations
Book•
1 Jan 2005
TL;DR: This chapter discusses Cluster Analysis-Partitioning, an Introduction to Branch-and-Bound Methods for Partitioning and Variable Selection for Regression Analysis.
Abstract: Cluster Analysis-Partitioning.- An Introduction to Branch-and-Bound Methods for Partitioning.- Minimum-Diameter Partitioning.- Minimum Within-Cluster Sums of Dissimilarities Partitioning.- Minimum Within-Cluster Sums of Squares Partitioning.- Multiobjective Partitioning.- Seriation.- to the Branch-and-Bound Paradigm for Seriation.- Seriation-Maximization of a Dominance Index.- Seriation-Maximization of Gradient Indices.- Seriation-Unidimensional Scaling.- Seriation-Multiobjective Seriation.- Variable Selection.- to Branch-and-Bound Methods for Variable Selection.- Variable Selection for Cluster Analysis.- Variable Selection for Regression Analysis.
159 citations
TL;DR: As what will be given by this branch and bound applications in combinatorial data analysis, how can you bargain with the thing that has many benefits for you?
Abstract: Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this branch and bound applications in combinatorial data analysis, how can you bargain with the thing that has many benefits for you?
117 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 1 |
| 2019 | 1 |
| 2017 | 1 |
| 2016 | 2 |
| 2015 | 1 |
| 2011 | 2 |