Analysis and design of financial data mining system based on fuzzy clustering.

With the rapid development of the economy, a large amount of financial data will be generated during the continuous growth of enterprises. However, due to the explosive growth of the financial data range index, the use of machine learning methods to mine and analyse financial data is extremely important. Among them, accurate financial risk evaluation is an effective measure to prevent and resolve corporate financial crises. In this article, we use fuzzy clustering method to establish a financial risk early warning and evaluation model. Specifically, we use fuzzy C‐mean (FCM), half‐suppressed FCM, and interval FCM clustering algorithms‐based state construction financial risk early warning and evaluation models, to give an evaluation from two aspects of corporate financial indicators and non‐financial indicators system. In order to verify the feasibility and effectiveness of the fuzzy clustering algorithms used in financial data mining, we conducted experiments in financial data mining and early warning in real estate companies and ST companies. The experimental results show that the fuzzy clustering algorithms represented by the FCM clustering algorithm has achieved good results in financial data mining, and can achieve good results in financial risk analysis and financial risk early warning. [ABSTRACT FROM AUTHOR]