In model-based clustering and classification, the cluster-weighted model is a convenient approach when the random vector of interest is constituted by a response variable $$Y$$ and by a vector $${\varvec{X}}$$ of $$p$$ covariates. However, its applicability may be limited when $$p$$ is high. To overcome this problem, this paper assumes a latent factor structure for $${\varvec{X}}$$ in each mixture component, under Gaussian assumptions. This leads to the cluster-weighted factor analyzers (CWFA) model. By imposing constraints on the variance of $$Y$$ and the covariance matrix of $${\varvec{X}}$$ , a novel family of sixteen CWFA models is introduced for model-based clustering and classification. The alternating expectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family, is described; to initialize the algorithm, a 5-step hierarchical procedure is proposed, which uses the nested structures of the models within the family and thus guarantees the natural ranking among the sixteen likelihoods. Artificial and real data show that these models have very good clustering and classification performance and that the algorithm is able to recover the parameters very well.

Sanjeena Subedi

Antonio Punzo

Salvatore Ingrassia

Paul D. McNicholas

Advances in Data Analysis and Classification

国际期刊《数据分析与分类进展》（ADAC）旨在作为一个论坛，提供有关从多种类型数据中提取已知方面的研究和应用的高标准出版物。它发表有关数据分析的结构、定量或统计方法等主题的文章；分类、聚类和模式识别方法的进展；对复杂数据进行建模和挖掘大型数据集的策略；从数据中提取知识的方法，以及在特定实践领域中应用先进方法。文章说明了如何通过熟练使用数据分析方法从数据中获取新的特定领域知识。该杂志还发表概述和阐明特殊方法的基本思想和技术的调查论文。

The international journal "Advances in Data Analysis and Classification" (ADAC) aims to provide a high-standard forum for research and applications focused on extracting known aspects from various types of data. It publishes articles on topics such as the structure, quantitative, or statistical methods of data analysis; advancements in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large datasets; methods for extracting knowledge from data; and the application of advanced methods in specific practical fields. The articles illustrate how to derive new domain-specific knowledge from data through the proficient use of data analysis methods. The journal also publishes survey papers that outline and clarify the fundamental ideas and techniques of special methods.

国际期刊《Advances in Data Analysis and Classification》（ADAC）旨在提供一个高标准的论坛，专注于从多种类型的数据中提取已知信息的研究与应用。期刊发表关于数据分析的结构、定量或统计方法的文章；分类、聚类和模式识别方法的最新进展；对复杂数据建模和大数据集挖掘的策略；从数据中提取知识的方法，以及在特定实践领域中应用先进方法的研究。文章展示了如何通过熟练运用数据分析方法，从数据中获取新的领域知识。此外，期刊还发表对特殊方法的基本思想和技术的综述性论文。

Clustering and classification via cluster-weighted factor analyzers

A novel family of sixteen CWFA models is introduced for model-based clustering and classification, with alternating expectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family.

Category	Quartile
计算机科学	4
计算机科学, 统计学与概率论	4

Clustering and classification via cluster-weighted factor analyzers

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Journal Info

Category	Quartile
STATISTICS & PROBABILITY	2