基于规则的离群数据挖掘在病虫害预测中的应用  

Application of Rule-based Outlier Data Mining to Pest and Disease Forecasting

作　　者：王红霞[1,2] 卫一唯[1] 陈俊杰[1] 王志伟[3] 

机构地区：[1]太原理工大学计算机与软件学院,太原030024 [2]山西水利职业技术学院,太原030027 [3]山西省气候中心,太原030006

出　　处：《电脑开发与应用》2009年第12期41-43,共3页Computer Development & Applications

基　　金：山西省气象局开放式研究基金资助项目(sx072001)

摘　　要：描述了离群数据挖掘的基本理论以及经典算法,提出附加约束的基于规则的离群数据挖掘算法,并根据过去几十年数据的特点,提出了一种运用离群数据挖掘进行病虫害预测的模型。实验证明,通过对实际病虫害气象数据进行挖掘,预测的结果合理,预测效率提高。In the article described the stray data mining elementary theory as well as the classical algorithm,and according to the characteristics of data in the past dozens years, they made a theory that the use of outlier data mining models to predict diseases and insect pests. The experiment proved that through the excavation of the experimental data, the result of forecast is reasonable, so that the forecast efficiency of the diseases and insect pests can be improved.

关 键 词：数据挖掘 离群数据 离群数据挖掘 病虫害预测 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]