New Optimization Techniques in Data Mining

这项拨款为开发用于数据挖掘的新型优化工具提供资金。这项工作将侧重于整合来自不同来源、不同可靠性水平的输入。这些输入将被允许在一定程度上影响模式，这取决于归因于这些输入的置信水平。开发的优化和算法也将依赖于成对比较或分离措施，而不是单独依赖属性的参数映射。数据挖掘的结果，以分类的形式，将是惩罚最小化目标的最优解。偏离更可靠的观点和两两比较的惩罚会更高，而偏离不可靠来源的观点和两两比较的惩罚会更小。将测试这一系列技术在患者预后方面与现有方法的有效性；客户细分;以及国家或公司信用评估。测试将导致校正和微调惩罚函数适合在不同的上下文中使用。如果成功，数据挖掘技术有望对模式识别和获取专家知识的方法产生影响。它将能够结合并包括专家评估以及经验数据和科学理论预测，每一个都有助于最终的模式结果，这取决于对每个来源输入的信心。这项研究的潜在应用包括金融工程和医疗保健。

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.