Clustering - Visualization, Validation and Response Oriented Methods

项目摘要：聚类-可视化，验证和面向响应的方法rebecka Jornsten， pi本项目重点研究两种新的普遍适用的聚类方法。提出了一种面向响应的解释变量聚类方法——面向响应的变量聚类（ROVAC）。该方法通过套袋的新应用，使用响应模型来生成变量聚类。聚类不依赖于解释变量的分布假设。该方法是灵活的，允许使用不同的模型选择标准。它可以推广到各种各样的响应模型。PI还在研究最近开发的聚类可视化和验证工具的扩展，即相对数据深度（ReD）。PI以相对于回归的深度概念为基础，开发了选择数据集中集群数量和选择与特定集群相关的特征的方法。这个项目很大程度上是由跨学科研究推动的。目标是为相关领域的科学家提供新的、灵活的聚类工具来分析高维数据。对特征进行聚类或分组的标准方法需要定义相似性度量。这通常是一项重要且高度主观的任务。在这个项目中，PI专注于开发两种基于直观简单概念的聚类技术。第一种方法使用另一个被测量量的知识，即响应。该方法将与响应相似的特征分组在一起。第二种方法使用深度的概念，衡量一个特征相对于它的组的代表性。原型算法正在实际数据上实现，其中包括但不限于基因表达数据的示例。初步结果与目前领先的方法具有竞争力。

Project Abstract:Clustering - Visualization, Validation and Response Oriented MethodsRebecka Jornsten, PIThis project focuses on two new universally applicable methods for clustering. A method is proposed for the clustering of explanatory variables in a response oriented fashion, ROVAC (Response Oriented Variable Clustering). The method uses a response model to generate the variable clustering, via a novel application of bagging. The clustering does not rely on a distribution assumption for the explanatory variables. The method is flexible, allowing for the use of different model selection criteria. It generalizes to a wide variety of response models. The PI is also investigating extensions of a clustering visualization and validation tool recently developed, the Relative Data Depth (ReD). Building on the concept of the depth relative to regression the PI develops methods for selecting the number of clusters in a data set and selecting the features that are related to a specific clustering.This project is largely motivated by interdisciplinary research. The goal is to provide scientists in related fields with new and flexible clustering tools for analyzing high-dimensional data. Standard methods for clustering or grouping of features require the definition of a measure of similarity. This is often a non-trivial and highly subjective task. In this project the PI focuses on the development of two clustering techniques based on intuitively simple concepts. The first method uses the knowledge of another measured quantity, a response. The method groups features together that are similarly related to the response. The second method uses a concept of depth, a measure of how representative a feature is with respect to its group. Prototype algorithms are being implemented on real data with examples from, but not limited to, gene expression data. Preliminary results are competitive with current leading methodologies.