New Optimization Techniques in Data Mining

数据挖掘中的新优化技术

• 批准号: 0620677
• 负责人: Dorit Hochbaum
• 金额: $ 33.05万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620677&HistoricalAwards=false
• 关键词: New; Optimization; Techniques; Data; Mining

This grant provides funding for the development of novel optimization tools to be used for data mining. The work will focus on the incorporation of inputs from disparate sources with different levels of reliability. These inputs will be allowed affect the patterns to a degree that depends on the confidence level attributed to the inputs. The optimization and algorithms developed will also rely on pairwise comparisons, or separation measures, rather than on parametric mapping of the attributes alone. The data mining outcome, in the form of classification, will be an optimal solution to a penalty minimization objective. The penalty is assigned to be higher for deviating from opinions and pairwise comparisons that are more reliable and it will be smaller penalty for opinions and pairwise comparisons for less reliable sources. This family of techniques will be tested for effectiveness against existing methodologies in areas of patient prognosis; customer segmentation; and country or firm credit assessment. The testing will result in calibration and fine tuning of the penalty functions appropriate for use in different contexts.If successful, the data mining techniques are expected to have impact on pattern recognition and on methodologies for capturing expert knowledge. It will enable to incorporate and include expert assessments along side empirical data, and scientific theory predictions each contributing to the final pattern outcome depending on the confidence in the input from each source. Potential applications of the research include financial engineering and health care.

这笔赠款为开发用于数据挖掘的新型优化工具提供资金。这项工作将侧重于纳入来自不同来源、可靠程度不同的投入。 这些输入将被允许影响模式到一定程度，这取决于归因于输入的置信水平。 开发的优化和算法还将依赖于成对比较或分离措施，而不是单独依赖于属性的参数映射。 以分类形式的数据挖掘结果将是惩罚最小化目标的最优解。 对于偏离更可靠的意见和成对比较，惩罚被分配得更高，对于不太可靠的来源，意见和成对比较的惩罚将更小。这一系列的技术将测试的有效性对现有的方法在病人预后领域;客户细分;和国家或公司的信用评估。测试结果将校准和微调适用于不同情况的惩罚函数，如果成功，预计数据挖掘技术将对模式识别和获取专家知识的方法产生影响。 它将能够结合和包括专家评估沿着经验数据和科学理论预测，每个都有助于最终的模式结果，这取决于对来自每个来源的输入的信心。 该研究的潜在应用包括金融工程和医疗保健。