Discovering Innovation and Entrepreneurship Opportunities for College Students Using Big Data Mining Algorithms

The purpose of this research is to explore the feasibility of extracting invention and entrepreneurship insight through big data mining algorithms opportunities among university undergraduates. Technology is developing very fast, and the market is also changing its shape gradually; therefore, the application of big data analytics becomes critical to recognize new trends and preferences. The methodology involves clustering, classification, and association rule mining for analyzing various data sets that include social media, academic publications, and marketers’ reports. From clustering, we identified clusters that correspond to the key market segment, such as sustainable technology and personalized health care, to direct students into feasible entrepreneurial ventures. Classifier algorithms, particularly Support Vector Machines, showed a high level of performance in the prediction of entrepreneurial opportunities from previous data and, hence, the ability to make good decisions in line with history. Using the Association rule mining technique, strong correlations between market variables were found, thus enabling students to build strong business strategies that respond to diverse consumer needs. This paper’s results demonstrate how big data mining is able to expand the educational paradigm and develop a data-driven entrepreneurial perspective among students to support the existing gap in academic research and real world Entrepreneurial practicality.