Data Mining of Coal Mining Gas Time Series and Knowledge Discovery
Use the data mining techniques to discover the regularity knowledge from the gas sensor monitoring history database is very important approach for the supervisors to identify the reason causing the exceptional fluctuation automatically and make the correct decisions promptly. The clustering method based on the DTVV distance for the gas time series above the critical level is proposed firstly, thus seven typical exceptional time series patterns can be obtained. From which the important shape indexes can be extracted and filtered based on piecewise shape measure method. At last, the regularity knowledge used to recognize the exceptional pattern of gas time
data mining time series clustering shape measure knowledge discovery
Shisong Zhu Yunjia Wang Lifang Kong
Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining& Tec Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining& Tec Xuzhou Air Force College, Xuzhou, 221000, Jiangsu, China
国际会议
杭州
英文
686-689
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）