Unsupervised learning of finite mixture models in data mining applications

数据挖掘应用中有限混合模型的无监督学习

• 批准号: DP0345901
• 负责人: Prof Geoffrey McLachlan
• 金额: $ 4.44万
• 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: 2003
• 资助国家: 澳大利亚
• 起止时间: 2003-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0345901
• 关键词: Unsupervised; learning; finite; mixture; models

The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.

从海量的大型数据库中提取有用的信息称为数据挖掘。其广泛但模糊的目标是在数据中找到“有趣的结构”，这通常会导致将数据划分为多个簇。为此，我们考虑了在不需要任何结构先验知识的情况下，在Hugh数据集中快速、高效和自动地学习有限混合模型。这种在数据挖掘应用中发现和验证群结构的概率方法将极大地增强科学、工业和政府的知识管理和决策支持。