Clustering U.S. 2016 Presidential Candidates Through Linguistic Appraisals

Pozo, Raquel González del; García-Lapresta, José Luis; Pérez-Román, David

doi:10.1007/978-3-319-66824-6_13

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…В информационной поддержке принимаемых решений успешно используется технология кластерного анализа в таких областях, как машиностроение [7], экономика (сегментирование рынка [8], стратегическое планирование [9], анализ кредитных рисков [10]), государственное и муниципальное управление (региональная экономическая поли-тика [11], налоговое администрирование [12], избирательные кампании [13]), медицина [14,15], образование [16,17] и др. Еще более широкое распространение получила кластеризация в составе различных информационных технологий, например, распознавания изображений [18], анализа текстов [19], отражения кибератак [20].…”

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Genetic clustering algorithm

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2020

Rossijskij tehnologičeskij žurnal

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The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making. The algorithm makes it possible to account for different preferences of the analyst in clustering reflected in a calculation formula of fitness function. The place of this algorithm among those used for cluster analysis has been shown. The algorithm is simple in its program implementation, which increases its usage reliability. The used technology of evolutionary modeling is rather expanded in the mentioned algorithm. Firstly, the decimal chromosomes coding is used instead of the traditional binary coding. This has resulted from the fact that the chromosome genes condition is multiple and not binary. Moreover, this is due to the absence of the genetic operator of inversion in this algorithm. Secondly, a new genetic operator used for filtering has been implemented. This operator eliminates chromosomes that do not meet the required clusters quantity condition in a task. Such chromosomes can appear in the stochastic process of their evolution. The presented algorithm has been studied in a series of simulation experiments. As a result, it has been found that stabilization of splitting into clusters is reached when the number of completed generations of evolution is 200 and more, and the population size is rather small: from 150 chromosomes (in this case no considerable amount of random-access store is required). The calculations carried out on real data showed for this algorithm the high quality of clustering and the acceptable computing speed of the same order with the computing speed of SOM and “k-means” algorithms.

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Genetic clustering algorithm

Анфёров

2020

Rossijskij tehnologičeskij žurnal

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A family of distances for preference–approvals

et al. 2022

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A preference–approval on a set of alternatives consists of a weak order on that set and, additionally, a cut-off line that separates acceptable and unacceptable alternatives. In this paper, we propose a new method for defining the distance between preference–approvals taking into account jointly the disagreements in preferences and approvals for each pair of alternatives. The proposed distance is compared to the existing distance functions to deal with clustering problems. Specifically, we prove that our metric improves the estimated clusters in terms of both stability and accuracy.

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Clustering U.S. 2016 Presidential Candidates Through Linguistic Appraisals

Cited by 2 publications

References 12 publications

Genetic clustering algorithm

Genetic clustering algorithm

A family of distances for preference–approvals

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