Stock selection is stock market participants facing a difficult problem. This article aims to apply clustering in data mining techniques to analyze the financial data and stock trading data, give the classification of stocks, provide the decision support of stock selections for the dealer. The main work include: the construction of the financial data and stock market data, the pre-processing of the data in the data set, the analysis of the data set applying the clustering techniques, giving the stock's classification, verifying the classification results. The stock selection base on cluster analysis improves the success rate and yield, and have important practical value of guidance of investment decisions.
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Ruizhong Wang (2011) studied this question.