基于Apriori算法的森林虫害预测方法

针对现阶段对已出现森林虫害数据未能完成全面、及时地统计,以及难以准确预测森林虫害爆发的潜在外来诱因的问题,提出使用面向Web挖掘的主题网络爬虫搜集病虫害相关数据,并利用大数据挖掘频繁模式与关联规则的Apriori算法,挖掘结果得到满足最小支持度阈值的频繁2项集,并进一步从中筛选2种重要的特征子集,包括害虫与寄主之间的频繁模式,寄主与外来树种之间的频繁模式。解决了已出现的病虫害数据难以统计的难题;同时预测出针对某一地区害虫可能诱发森林虫害的外来树种。结果表明该方法能达到可靠、有效的森林虫害预测目的。

Forest Pest Prediction Method with Apriori Algorithm

The experiment was conducted to solve the problem that the forest pest data failed to complete the comprehensive and timely statistics,and it is difficult to accurately predict the potential extrinsic incentive of forest pest outbreaks.It was proposed to use the web-based reptile for Web mining to collect pest and disease-related data,and use large data Apriori algorithm of mining frequent patterns and association rules to extract the frequent 2 itemsets in satisfying the minimum support thresholds,and two important feature subsets were selected including the frequent patterns between pests and hosts,and the host and alien species between the frequent patterns.The experiment solved the pests and diseases in statistics,and predicted the alien species from the induced forest pests in a certain area.