Classification methods for providing personalised and class decisions

提供个性化和类别决策的分类方法

• 批准号: DP180101192
• 负责人: Prof Geoffrey McLachlan
• 金额: $ 23.95万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2018
• 资助国家: 澳大利亚
• 起止时间: 2018-09-30 至 2022-09-29
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP180101192
• 关键词: Classification; methods; providing; personalised; class

This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. The project will use a statistical approach to automatically match the clusters, since the overall mixture model provides a template for the class. It will provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.

该项目提供了一种新颖的方法来对类中实体上的多变量样本进行聚类，该方法可以自动匹配跨实体的样本簇，从而允许类中样本之间的样本间变化。该项目旨在开发一个广泛适用的、基于混合模型的框架，用于类中具有样本间变化的多变量样本的同时聚类，以及类中跨实体的聚类匹配。该项目将使用统计方法自动匹配集群，因为整体混合模型为类提供了模板。除了识别样本内的非典型数据点和类别内的异常样本外，它还将为区分不同类别提供基础。主要应用包括生物图像分析和流式细胞术中的数据分析，流式细胞术是生命科学家的基础研究工具之一。