粗糙集模糊聚类分析法在昆虫分类研究中的应用  

Application of rough-set theory and fuzzy clustering analysis in insect taxonomy

作　　者：杜瑞卿[1] 张征田[1] 刘广亮[2] 武福华[1] 

机构地区：[1]南阳师范学院生命科学系,河南南阳473061 [2]南阳师范学院计算机系,河南南阳473061

出　　处：《昆虫学报》2006年第1期106-111,共6页Acta Entomologica Sinica

基　　金：南阳师范学院科研资助项目(NYCT2004K01)

摘　　要：本文根据昆虫图像,对半翅目、鳞翅目、鞘翅目的28种昆虫提取的形状参数、叶状性、球状性等7项数学形态特征进行了粗糙集模糊聚类分析。在粗糙集处理的基础上,分别进行7指标和3指标(相对约简)两种不同的模糊聚类分析法相比较。结果显示,在作为目级阶元分类指标时,各项特征的重要性依次为:(似圆度、偏心率)>(亮斑数、球状性、圆形性)>(叶状性、形状参数);粗糙集分类正确率优于模糊聚类分析法;粗糙集处理后的3指标分类正确率优于未处理的7指标分类正确率。结论认为,粗糙集理论在昆虫依据数学形态特征进行分类方面与统计分析方法相比更有优势,粗糙集滤过指标后再进行模糊聚类法分析在昆虫分类研究上具有重要意义。Based on 7 math-morphological features （MMFs）, such as form parameter, lobation, sphericity, etc. extracted from the images of 28 species of insects of the Hemiptera, Lepidoptera and Coleoptera, the application of rough-set theory and fuzzy clustering analysis in insect taxonomy was evaluated. Then, based on the data prepared with rough-set theory analysis, fuzzy clustering analysis with 7 indexes or 3 indexes was made separately to assess their efficiency. The results showed that when used as indexes in taxonomy at the order level, the MMFs were ranked in the following order according to their importance： （hole number, sphericity, circularity） 〉 （ roundness, eccentricity） 〉 （ lobation, shape-parameter）. The classification correctness based on rough-set theory is higher than that based on fuzzy clustering analysis; and the correctness of fuzzy clustering analysis with 3 indexes based rough-set theory is also higher than that with 7 indexes. Evaluated by their application in insect taxonomy, the rough-set theory is more efficient compared with statistical analysis method. The method of fuzzy clustering analysis with the index filtrated by rough-set theory has high application prospect in insect taxonomy.

关 键 词：昆虫分类 粗糙集 模糊聚类 数学形态特征 

分 类 号：Q966[生物学—昆虫学]