Data Mining of Coal Mining Gas Time Series and Knowledge Discovery
Shisong Zhu, Yunjia Wang, Lifang Kong · 2011
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 DTW 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 series can be abstracted from the shape feature table and represented with the first order predicate logic language. As an example, the important promotion application value of this set of method using in a high gas coal mine is proved.