Research On The Application Of Data Mining Technology In Stock Price Analysis

At present, with the prosperity and development of the market economy, innovative financial system, the pattern of global economic integration and gradually improve, more financial products become the people’s financial instruments, while the stock one of the most common operations risk investment products, is quietly into people’s lives. Equity securities as the most representative securities, stock data analysis and prediction is lively topic in today’s world. Stock data analysis is often dependent on a large number of historical transaction data to predict how well this growing application of huge historical data, to dig out in the law of value, is more interested in the topic of every investor is faced with stock prediction the biggest problem. More mature fundamental analysis and technical analysis on the stock market varied, often also cannot do it.This article from the actual characteristics of the stock market point of view, the application of data mining technology has matured, the stock attributes apply the ID3 algorithm to classify and ID3 algorithm for the problems, the improved algorithm, attribute information gain improved deep recursion ID3 algorithm and the weighted average method to determine the parameters of the weight domain knowledge preferred policy analysis on individual stocks, dig out the inherent value of stocks between the law; in addition, the association rules algorithm used in the stock "plate linkage" laws, and for the classic Apriori defect existed proposed algorithm has been improved, Apriori improved algorithm is more flexible, more suitable for mining the relationship between the different sections.Examples prove that the improved algorithm can better carry out data mining analysis for the stock, but also a marked improvement on the efficiency of the algorithm, to a certain extent, make up for the shortcomings of traditional analysis method, to provide investors with valuable stock data mining methods.