摘要
借鉴物理学中动力学原理,提出基于动力学理论的聚类参数挖掘策略,并应用于银行贷款数据风险评估。定义了聚类动力学参数挖掘概念、g-平均、簇的-相似、风险相似度等概念,提出基于聚类动力学参数挖掘的聚类策略挖掘算法CSMA(clustering strategy mining algorithm),分析了该策略在不同参数下对实验结果的影响。实验结果表明,CSMA策略使得聚类分析的精度提高了9%～13%。
Inspired by the theory of dynamic in physics, a novel strategy of clustering based on dynamic parameter mining is proposed, and is applied to the risk evaluation system of bank loan. The main contributions include： Concept of clustering based on dynamic para- meter mining are defined, the concepts over bank loan databases, such as g-mean, the 0-similar of cluster, and risk similarity, a CSMA （clustering strategy mining algorithm） algorithm based on dynamic parameters mining are proposed, the effect on different parameters is analyzed. Extensive experiments demonstrate that CSMA algorithm is effective in clustering, and the precision is improved by 9%-13%.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第14期3478-3480,F0003,共4页
Computer Engineering and Design
基金
国家自然科学基金项目(60773169)
