用两两比较表示的数据聚类

Q4 Engineering

Control and Cybernetics Pub Date : 2022-09-01 DOI:10.2478/candc-2022-0021

S. Dvoenko

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引用次数: 0

摘要

本文考虑以两两比较形式给出的实验数据，如距离或相似性。处理此类数据的聚类算法是基于众所周知的k-means过程开发的。给出了与因子分析的关系。在两两比较的情况下，考虑了提高聚类质量和找到适当数量的聚类的问题。提供了说明性示例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Clustering of Data Represented by Pairwise Comparisons

Abstract In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.

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来源期刊

Control and Cybernetics 工程技术-计算机：控制论

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： The field of interest covers general concepts, theories, methods and techniques associated with analysis, modelling, control and management in various systems (e.g. technological, economic, ecological, social). The journal is particularly interested in results in the following areas of research: Systems and control theory: general systems theory, optimal cotrol, optimization theory, data analysis, learning, artificial intelligence, modelling & identification, game theory, multicriteria optimisation, decision and negotiation methods, soft approaches: stochastic and fuzzy methods, computer science,