Parsimonious consensus hierarchies, partitions and fuzzy partitioning of a set of hierarchies

IF 1.6 2区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2024-04-12 DOI:10.1007/s11222-024-10415-7

Ilaria Bombelli, Maurizio Vichi

引用次数: 0

Abstract

Methodology is described for fitting a fuzzy partition and a parsimonious consensus hierarchy (ultrametric matrix) to a set of hierarchies of the same set of objects. A model defining a fuzzy partition of a set of hierarchical classifications, with every class of the partition synthesized by a parsimonious consensus hierarchy is described. Each consensus includes an optimal consensus hard partition of objects and all the hierarchical agglomerative aggregations among the clusters of the consensus partition. The performances of the methodology are illustrated by an extended simulation study and applications to real data. A discussion is provided on the new methodology and some interesting future developments are described.

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一组层次结构的准共识层次结构、分区和模糊分区

介绍了将模糊分区和准共识层次结构（超对称矩阵）拟合到同一组对象的层次结构中的方法。描述了一个模型，该模型定义了一组分级分类的模糊分区，分区中的每个类都由一个准共识层次结构合成。每个共识包括对象的最佳共识硬分区和共识分区各簇之间的所有分层聚合。该方法的性能通过扩展的模拟研究和真实数据的应用得到了说明。此外，还对新方法进行了讨论，并介绍了一些有趣的未来发展。

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来源期刊

Statistics and Computing 数学-计算机：理论方法

CiteScore

3.20

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.

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