Research On Mining Stock Investment Value Analysis Of Clustering

With the rapid development of China’s stock market, and the explosive growth of data, many valuable informations hide behind huge amounts of data, and these informations are often hard to find by experiences and naked eyes. In this complicated financial situation, application of data mining method in the analysis of the stock market becomes more and more important, it uses the relevant data and data analysis tools to find relationships between data, the results can be used to predict and make reasonable decisions.As an emerging field, data mining has been reflects its value in many industrys. Applied to B end and C end of the commercialization of the data mining system is the emergence of large-scale, at the same time, some famous also embedded in the function of data mining in data analysis software components, such as SAS, SPSS, SAP, ORACLE, etc. And the aspects of the finance data mining is a challenging research topic, because of the uncertainty and randomness of financial data, unpredictability, led to the association rules between data are difficult to find, how to extract the hidden value from the data, becomes the key to the dominant position in the financial markets.Clustering analysis method, as an important branch in data mining, the goal is to study the similarity between the data, divide the similar data for the same class. Through cluster analysis, therefore, can measure the similarity between the stocks, to grasp the general trend of stock, to judge the potential value of the shares. Similarly, time series analysis is widely used in the field of financial data mining, the time series of the stock price curve is the most typical by time series analysis, can get the rule of data changes over time, and the development trend of the future. In the stock market, therefore, we can through the above two methods of data mining, clustering analysis for similar stock classification, time series analysis and reasonable projections for the future stock price movements.In conclusion, this article in the first chapter mainly expounds the application of data mining in stock analysis, of the current state of research on the connotation of the value analysis in chapter 2, this paper puts forward a value analysis framework, in all aspects of value and price analysis, around the price will return value is studied, as mining shares valuable theoretical framework, the third chapter, and the application of the fourth chapter is our part, in the third chapter, we selected the 10 shares of the listed company as an example, only around a framework to analyze the value of the second chapter proposes a combined application of clustering analysis and relative value theory of data mining method, the financial indicators of the listed company value clustering, according to the overall effect and individual effect law puts forward all kinds of stock price movements in anticipation of the hypothesis, and the application of time series analysis in the fourth chapter holt double parameters exponential smoothing method is used for all kinds of relative value curve is forecasted, and the inspection before assumptions, the results proved that the method is valid. The purpose of this model is to dig out the valuable stock, looking for investment opportunities and avoid risks, for the excessive speculation of the stock market increase a reasonable method, make reasonable decisions. The results showed that the method of data mining in the stock market has a certain theoretical significance and application prospects.