Research and realisation of similar information high precision purification and mining technology. (2018) - Zhou, Ruiling
Similar information purification and mining methods in the past are generally of low precision and weak usability. Therefore, we propose a method to dynamically update time series, that is, a similar information high precision purification and mining method based on time series updating. The method is used to implement regional linear time similar information time series by using rise analysis and linear regression analysis. Extreme value standardisation method is used to collate linear region so that the data in time series can be compared in parallel and the description of similar information feature is realised. Vertically align the two head-ends of time series to be purified; high precision purification is achieved by calculating the similarity of characteristics similarity displacement representation between two segments of similar information. Experimental verification shows that compared with previous methods, the recall F * value is the highest among different methods for five dataset, and the time cost of the proposed method is shorter than other methods. It was believed that .purification and mining performance of the proposed method is stronger with shorter time cost.