Stability Analysis for Clustering

聚类稳定性分析

• 批准号: 69240513
• 负责人: Professor Dr. Joachim M. Buhmann
• 金额: --
• 依托单位: Departement Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2011-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/69240513?language=en
• 关键词: Stability; Analysis; Clustering

A new model validation principle based on information theory is developed and analyzed in this project. Discrete structures like data partitions in clustering or graph cuts are infered from noisy data according to an objective function. Due to the noise in the measurements (data), learning algorithms have to return a set of approximate partitionings which are considered to be statistically indistinguishible. The uncertainty in the data induce a quantization of the space of partitionings and, thereby, defines a coding scheme. An information theoretic analysis of this code yields an approximation capacity of the underlying model represented by an objective function. This selection criterion trades informativeness against stability and controls the model complexity by the approximation precision. Approximate solutions are sampled by Gibbs sampling at a finite temperature. This novel information theoretic model selection principle will be applied to correlation clustering in the context of clustering protein interaction data. Furthermore, we will apply this principle for learning dynamical systems in systems biology and for infering user roles in information security applications.

本计画提出并分析了一种新的基于资讯论的模型确认原理。离散结构，如聚类中的数据划分或图切割，根据目标函数从噪声数据中推断。由于测量（数据）中的噪声，学习算法必须返回一组被认为是统计上不可分割的近似划分。数据中的不确定性引起分区空间的量化，从而定义编码方案。该代码的信息理论分析产生的目标函数表示的基本模型的近似能力。该选择准则在信息量与稳定性之间进行权衡，并通过逼近精度控制模型的复杂性。近似解在有限温度下通过Gibbs抽样进行抽样。这种新的信息理论模型选择原则将被应用到相关聚类的背景下，聚类蛋白质相互作用数据。此外，我们将应用此原理学习系统生物学中的动态系统，并推断信息安全应用中的用户角色。