Extended multivariate comparison of 68 cluster validity indices. A review

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-03-25 DOI:10.1016/j.chemolab.2024.105117

Roberto Todeschini, Davide Ballabio, Veronica Termopoli, Viviana Consonni

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引用次数: 0

Abstract

Clustering is an unsupervised machine learning methodology widely used in several sciences to find groups of similar patterns in complex data. The results generated by clustering algorithms generally depend on user-defined input parameters such as the number of expected clusters, which can have a great impact on the homogeneity of the identified clusters.

Clustering validity indices (CVIs) are an effective method for determining the optimal number of clusters that best fit the natural partition of a dataset. They do not require any underlying assumption nor a priori knowledge about the true dataset structure. Since 1965, many cluster validity indices have been proposed in the literature and used in several different applications.

In this paper, the performance of 68 cluster validity indices was evaluated on 21 real-life research and simulated datasets. CVIs were compared on the same partition for each dataset, which was searched for by the k-means clustering algorithm. Multivariate chemometric methods were applied to disclose mutual relationships among the indices and to select those that are more effective in terms of accuracy and reliability.

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68 个聚类有效性指数的扩展多元比较。综述

聚类是一种无监督的机器学习方法，被广泛应用于多个科学领域，用于在复杂数据中发现相似模式的群组。聚类算法生成的结果通常取决于用户定义的输入参数，如预期聚类的数量，这可能会对所识别聚类的同质性产生很大影响。聚类有效性指数（CVI）是确定最适合数据集自然分区的最佳聚类数量的有效方法。聚类有效性指数（CVI）是确定最适合数据集自然分区的最优聚类数量的有效方法，它不需要任何基本假设，也不需要关于真实数据集结构的先验知识。自 1965 年以来，文献中提出了许多聚类有效性指数，并将其用于多种不同的应用中。本文在 21 个实际研究和模拟数据集上评估了 68 个聚类有效性指数的性能。在每个数据集的相同分区上对 CVI 进行了比较，该分区通过 k-means 聚类算法进行搜索。应用多元化学计量学方法来揭示指数之间的相互关系，并选出在准确性和可靠性方面更有效的指数。

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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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.