国防科技大学学报2011，Vol.33Issue(3)：164-168,5.
LRE试车数据挖掘中基于最大散度差的模糊聚类分析方法
Fuzzy Cluster Analysis Based on Maximum Scatter Difference in LRE Test Data Mining
摘要
Abstract
In the clustering analysis of test data of liquid rocket engine, in order to solve the problem that there is insignificant difference between fault sample and normal sample, the maximum scatter difference criterion was introduced and the maximum scatter difference based clustering algorithm ( MSD-CA) was presented. In MSD-CA, the similarity of samples was measured by divergence, and the minimizing of the within-class divergence and maximizing of the between-class divergence were processed together. After that, fuzzy theory was introduced to maximum scatter difference criterion, and the maximum scatter difference based fuzzy clustering algorithm (MSD-FCA) was presented and used to do "soft partition'' for test data to improve the precision of cluster. The method is verified with experimental results.关键词
液体火箭发动机/试车数据/数据挖掘/最大散度差准则/软划分/模糊聚类Key words
liquid rocket engine/ test data/ data mining/ maximum scatter difference criterion/ soft partition/ fuzzy clustering分类
信息技术与安全科学引用本文复制引用
王珉,胡茑庆,秦国军..LRE试车数据挖掘中基于最大散度差的模糊聚类分析方法[J].国防科技大学学报,2011,33(3):164-168,5.基金项目
国家自然科学基金赍助项目(50675219) （50675219）
湖南省杰出青年科学基金资助项目(08JJ1088) （08JJ1088）
