MINING AGRICULTURE DATESET WITH BACKGROUND KNOWLEDGE

摘要：

The wide availability of huge amounts of data has generated an urgent need for the research of data mining. Background knowledge can make the mining more effective. As the analysis of agriculture dataset is usually a complex work, more domain knowledge is to be utilized. Two approaches for mining agriculture dataset with domain knowledge is proposed. One utilizes the life cycle of borer to intelligently filter out the data that will hamper the mining process. Another finds classification-based outliers, by detecting the change of the classification result, based upon the links between attributes and error model. Both of them are successful.

关键词： knowledge acquisition constraints learning algorithms time-series analysis classification

作者: Zhenfeng He

作者单位: Department of Computer Science, Fuzhou University, Fuzhou, 350002, China

会议类型: 国际会议

会议名称: 第三届智能化农业信息技术国际学术会议(The 3rd International Symposium on Intelligent Information Technology in Agriculture)(ISIITA)

会议地点: 北京

会议语种:英文

页码: 481-484

在线出版日期: 2005-10-14（万方平台首次上网日期，不代表论文的发表时间）