一种获取渔场知识的数据挖掘模型及知识表示方法研究

Research on data mining model for obtaining and representing fishery knowledge

提出了一种获取渔场知识的数据挖掘模型及知识表示方法。首先利用SVM(支持向量机)和模糊分类器从数据库中提取出有利于渔情预测的静态知识,然后通过可拓挖掘方法将静态知识转换为动态知识,最后采用本体构建技术对渔场的静态知识和动态知识进行表达,建立本体知识库。在上述方法的研究基础上,建立了以印度洋大眼金枪鱼为例的渔情预测原型系统。系统运行结果表明,提出的获取渔场知识的数据挖掘模型及知识表示方法是有效可行的。

This paper proposed a new knowledge discovery and representation model for fishery,which took three steps. Firstly,it extracted static knowledge from database by SVM （ support vector machine） and fuzzy classifier. Secondly,it used extension data mining method to transfer static knowledge into dynamic knowledge. Thirdly,it established an ontology knowledge base by utilizing a mapping mechanism between the dynamic knowledge and ontology. Using the proposed model building pro-cedure,implemented a prototype system for fishery forecasting. Experimental results show that the proposed method is effective and efficient.