Time Series Data Mining in Process Industry

摘要：

　　In recent years,there has been an explosion of interest in mining time series databases.In this paper,we make some attempts to mining process industrial time series.As with most computer science problems,representation of the data is the key to efficient and effective solutions.We introduce a novel algorithm,Trend Extremum Representation,which is empirically proved to be superior to Piecewise Linear Representation and Important Points Representation in manipulating large-scale industrial data.Then,subsequent mining procedure is undertaken.Through clustering analysis and association rule discovery,several useful rules are derived for differentiating normal and abnormal events in everyday operations.

关键词： time series data mining knowledge discovery trend extremum representation

作者: REN Jia GAO Jinfeng

作者单位: Institute of Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1069-1074

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）