Data Mining Of Coal Mine Gas Based On Quotien Space

Data mining is a course for mining latent, unknown but useful knowledge and information from practical data which is plentiful, incomplete, noisy, fuzzy and stochastic. Nowadays, the technologies of data mining are researched and applied to many fields including bank, telecom, insurance, traffic and so on. Classification and prediction are two kind of import forms for data analysis which can be used for constructing model that can describe the important data model or forecasting the data trend in future. In this article, classification and prediction methods of data mining was applied to predict gas concentrations which is the application of data mining coal and gas research. Predict changes in gas concentration is safety in coal mines of great significance.For more effective meteorological data mining, this thesis introduces the quotient space granular computing theory, grey model, structural machine learning algorithm and so on. Quotient space is introduced by Professor Zhang ling and Professor Zhang bo. This theory uses granular view to analyze and describe the world. To analyze the world from different granular levels is a good way to recognize things more comprehensive and reasonable, and it can reduce the computational complexity. For example ,the application in the field of heuristic search and path planning show its value for Practicality. Grey system model is formed by accumulated temporal data series and filters probable stochastic data from original data. It can find connotative rule from temporal data series and gain less stochastic but more disciplinarian data series to mine inherence character. The main character of structural machine learning is that its net structure and parameter should be constructed by processing practical data. That is to say that the net structure and parameter is not prearranged but by processing data.The main work of this thesis is listed below :(1)An overview of the development of granular computing theory and the basic model, focusing on the quotient space granular computing theory framework and structure of machine learning algorithms (covering algorithm).(2)Study of gas, several main models of data mining principles and implementation, this paper focuses on the trends in concentration and the characteristic concentration for different combinations of models.(3)Inadequate for the current prediction model, using a new combination gas data mining model, which first proposed by Professor Zhang Ling quotient space granular computing model for gas concentration characteristics of multi-layered particle size analysis, construction of gas concentration of commercial space model prediction using the quotient space theory of the nature and definition of the stratification of gas at different particle size characteristics of the complex time series of integrated features to make gas more specific data to better machine learning. The trend of concentration prediction, the paper uses a gray model GM (1,1). The characteristic concentration of the forecast is constructed of machine learning methods (covering algorithm), through a combination of both to predict the accuracy of gas concentration.(4)Through the gas in a coal mine in Shanxi Province to predict the test data, can analyze the combination of space-based business model has better prediction.