Research on Entrepreneurial Opportunity Identification Algorithm Based on Data Mining

This paper proposes an entrepreneurial opportunity identification algorithm based on data mining. The algorithm combines the efficiency and innovation of the FP-growth algorithm, aims to extract potential entrepreneurial opportunities from a large amount of business data, and provide accurate decision support for entrepreneurs and investors. First, this paper analyzes the basic needs of entrepreneurial opportunity identification and its challenges in practical applications, and proposes a solution based on frequent pattern mining. Then, an improved version of the FP-growth algorithm is designed in detail, the construction process of the traditional FP tree is optimized, and experimental verification is carried out on an entrepreneurial data set. By comparing the traditional Apriori algorithm, the improved FP-growth algorithm shows higher efficiency and lower computational complexity when processing large-scale data. In the model simulation part, this paper conducts extensive experiments using real entrepreneurial data and conducts precise data analysis. The improved FP-growth algorithm can effectively identify potential entrepreneurial opportunities in the market, and the recognition rate is more than 15% higher than that of traditional methods. Further data analysis shows the distribution of various entrepreneurial opportunities in different fields, providing entrepreneurs with practical market information.