Topics in Predictive and Descriptive Data Mining

预测性和描述性数据挖掘主题

• 批准号: 0204029
• 负责人: Jerome Friedman
• 金额: $ 42万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0204029&HistoricalAwards=false
• 关键词: Topics; Predictive; Descriptive; Data; Mining

AbstractPI: Jerry FriedmanDMS-0204029This proposal seeks support for research in predictive and descriptive data mining (DM). Decision tree methods are the most popular predictive DM tools. Research under this grant will investigate ways to overcome their most serious limitation: severe over fitting in the presence of categorical (factorial) predictor variables with very large numbers of values (factors). Cluster analysis is often used as a tool in descriptive DM. In most DM applications a large number of variables are measured on each observation. Usually clustering, if it exists, occurs only within (often small) unknown subsets of all the measured variables. Moreover, individual clusters may represent groupings on (possibly overlapping) variable subsets. The goal is to identify the clustered groups as well as the particular variable subsets on which each one preferentially clusters. Traditional clustering algorithms are not well suited for this task. Research under this grant will investigate new approaches for solving this problem, especially in situations where there are a very large number of measured variables.Data mining is used to discover patterns and relationships in data, with an emphasis on very large data bases. It has had a major impact in business, industry, science, medicine, and most recently homeland security. Data mining activities divide into two types: predictive and descriptive. Predictive DM involves using past observational data from a system to build a mathematical model of that system. The model is used to predict some future unknown property (attribute or variable) of the system, given other properties that will be known in the future. Descriptive DM seeks to construct compact, interpretable summaries of the data in order to understand patterns and relationships, without focusing on the prediction of particular attributes. This research will investigate new methodologies for increasing the power of both descriptive and predictive DM in problems for which they have been traditionally weak.

摘要本提案寻求对预测和描述数据挖掘（DM）研究的支持。决策树方法是最流行的预测决策工具。这项资助下的研究将研究如何克服其最严重的局限性：在具有大量值（因子）的分类（因子）预测变量存在时严重的过度拟合。聚类分析经常被用作描述性DM的一种工具。在大多数DM应用中，每次观测都测量大量变量。通常，如果存在聚类，则只发生在所有测量变量的未知子集内（通常很小）。此外，单个集群可以表示（可能重叠的）变量子集上的分组。目标是确定群集组以及每个组优先聚集的特定变量子集。传统的聚类算法不适合这个任务。这项资助下的研究将研究解决这一问题的新方法，特别是在有大量测量变量的情况下。数据挖掘用于发现数据中的模式和关系，重点是非常大的数据库。它对商业、工业、科学、医学以及最近的国土安全产生了重大影响。数据挖掘活动分为两种类型：预测性和描述性。预测性决策包括使用一个系统过去的观测数据来建立该系统的数学模型。该模型用于预测系统的某些未来未知的属性（属性或变量），给出未来已知的其他属性。描述性数据挖掘旨在构建紧凑的、可解释的数据摘要，以便理解模式和关系，而不关注特定属性的预测。这项研究将探讨新的方法，以增加描述性和预测性DM在传统上薄弱的问题中的能力。