Research on Mining Balanced Competition Strategy in Financial Market Based on Computer Data Mining Method

Abstract

The mining process of traditional market equilibrium competition strategy is difficult to deal with massive data, resulting in the inability to accurately classify customer data in the process of competition strategy customization. Therefore, this paper proposes a strategy formulation method of balanced competition in the financial market based on computer data mining. Firstly, the process of the k-means clustering algorithm was optimized, and Murkowski distance and Markov distance were used as classification basis to find some potential information hidden in the data. Based on the optimized K-means clustering algorithm for data processing, in order to achieve effective data analysis, design customer behavior data mining process and analyze customer value matrix and customer pyramid. Finally, the layout framework of a balanced competition strategy in the financial market is established. The results show that the classification accuracy of the design method is higher than that of the traditional method in different states.