The Application Of Data Mining In The Coal Price Forecast

Along with the development of modern science and technology, our society is more and more informatized and many large-scale database softwares are introduced into enterprises. It is in favor of criterion management of information. However, many problems go with huge data, and "information exploded, but poor knowledge" is well-known, which means that the information of society is large, but the application of them is little. Hence, the technologies of data mining are researched and applied to many fields including bank, telecom, insurance, traffic and so on. Forecast as the important part of data mining is studied by many researchers.In our economic society, the exact forecast is important because many right decisions of enterprises should be made out. In this dissertation, the technologies of data mining are discussed which are applied to coal price forecast., in where coal price means the purchasing-price for Power Plant. In our country, most of Power Plants are firepower plants, so coal is the primary energy sources. For power plants, the repertory of coal is important which affects the allocation of funds and assures the supply of electric power.Clustering and classification are the two different methods of forecast. In this dissertation, we mainly discuss about the forecast of coal price by using the two mothods.Clustering is one of the basic cognition actions. Objects are convenient for research by proper clustering. The k-means algorithm is a normal clustering method. And its disadvantage is that the value of k should be given in advance which affects the result of clustering. LBG is also a normal algorithm which has a better effect and a disadvantage that the clustering time is too long and it's easy to get into local minimum。Classification is another basic cognition function. As an important topic of data mining, data classification developed early in statistics, machine learning, artificial intelligence and so on. Recently, it is combined with database technologies to solve practical problems. There are many classification methods to forecast such as decision tree algorithm (C4.5),Bayes algorithm,BP algorithm and SVM. All of them have their own disadvantages. The first three should be improved in result and speed. Although the SVM has superior recognition rate especially for small samples and...