Discovering Innovation and Entrepreneurship Opportunities for College Students Using Big Data Mining Algorithms
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 9 |
| Year of Publication : 2024 |
| Authors : Zhiyuan Lyu, Yusri Kamin |
10.14445/23488379/IJEEE-V11I9P118
How to Cite?
Zhiyuan Lyu, Yusri Kamin, "Discovering Innovation and Entrepreneurship Opportunities for College Students Using Big Data Mining Algorithms," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 9, pp. 205-215, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I9P118
Abstract:
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.
Keywords:
Big data mining, Entrepreneurship, Innovation, College students, Data-driven decision-making.
References:
[1] Xiao Ma, and Hongli Pan, “Improving Entrepreneurial Skills and Professional Association of College Students Using Big Data Analysis and IoT,” Soft Computing, vol. 27, pp. 14253-14267, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Huan Yu, Ru Zhang, and Cheonshik Kim, “Intelligent Analysis System of College Students' Employment and Entrepreneurship Situation: Big Data and Artificial Intelligence-Driven Approach,” Computers and Electrical Engineering, vol. 110, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Haizhou Ma, and Aiping Ding, “Construction and Implementation of a College Talent Cultivation System under Deep Learning and Data Mining Algorithms,” The Journal of Supercomputing, vol. 78, pp. 5681-5696, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Huirong Zhu, and Qin Wang, “Design and Management of Microteaching Mode of Innovation and Entrepreneurship Education in Colleges and Universities Driven by Big Data,” Mobile Information Systems, vol. 2022, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Longyan Tan, and Feng Du, “Integrating Entrepreneurship and Innovation Education into Higher Vocational Education Teaching Methods Based on Big Data Analysis,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jinding Zou, “Intelligent Course Recommendation Based on Neural Network for Innovation and Entrepreneurship Education of College Students,” Informatica, vol. 46, no. 1, pp. 95-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yingnan Zhang, “Application of Data Mining Based on Improved Ant Colony Algorithm in College Students’ Employment and Entrepreneurship Education,” Soft Computing, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Lijun Bai, Chuanchao Wang, and Lili Zhang, “Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence,” Wireless Communications and Mobile Computing, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Rong Hu, and Jingwen Hu., “Construction and Analysis of College Students' Entrepreneurship Guidance Model from the Perspective of Ideological and Political Education under Big Data,” Mobile Information Systems, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Chao Wang et al., “Management and Entrepreneurship Management Mechanism of College Students Based on Support Vector Machine Algorithm,” Computational Intelligence, vol. 38, no. 3, pp. 842-854, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yu Wei, and Wanling Yang, “Quality Evaluation of College Students’ Innovation and Entrepreneurship Education Based on Grey Correlation Algorithm,” Application of Big Data, Blockchain, and Internet of Things for Education Informatization, pp. 345-355, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Liguan Chen, “Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yi Xu et al., “Investigating the Business Intelligence Capabilities’ and Network Learning Effect on the Data Mining for Start-Up's Function,” Information Processing & Management, vol. 59, no. 5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Shawni Dutta, Payal Bose, and Digvijay Pandey, “Exploring Entrepreneurial Mindset among University Students Using Machine Learning Approaches,” Artificial Intelligence and Information Technologies, 1st ed., CRC Press, 2024.
[Google Scholar] [Publisher Link]
[15] Kaiqiang An, “Exploration of Intelligent Teaching Methods for Ideological and Political Education in Colleges and Universities under the Background of ‘Mass Entrepreneurship and Innovation’,” International Journal of Antennas and Propagation, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Christian Rammer, and Nordine Es-Sadki, “Using Big Data for Generating Firm-Level Innovation Indicators-A Literature Review,” Technological Forecasting and Social Change, vol. 197, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Qinlei Zhu, and Hao Zhang, “Teaching Strategies and Psychological Effects of Entrepreneurship Education for College Students Majoring in Social Security Law Based on Deep Learning and Artificial Intelligence,” Frontiers in Psychology, vol. 13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Bailin Chen, Yi Liu, and Jinqiu Zheng, “Using Data Mining Approach for Student Satisfaction with Teaching Quality in High Vocation Education,” Frontiers in Psychology, vol. 12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yuan Wang et al., “Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method,” Mathematics, vol. 10, no. 8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xiaoling Shu, and Yiwan Ye, “Knowledge Discovery: Methods from Data Mining and Machine Learning,” Social Science Research, vol. 110, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ehsan Mohammadi, and Amir Karami, “Exploring Research Trends in Big Data across Disciplines: A Text Mining Analysis,” Journal of Information Science, vol. 48, no. 1, pp. 44-56, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Tina Asgari et al., “Identifying Key Success Factors for Startups with Sentiment Analysis Using Text Data Mining,” International Journal of Engineering Business Management, vol. 14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Jensen J. Zhao, and Sherry Y. Zhao, “Creativity and Innovation Programs Offered by AACSB-Accredited US Colleges of Business: A Web Mining Study,” Journal of Education for Business, vol. 97, no. 5, pp. 285-294, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Gang Zhou, and Rui Zhan, “Innovative Design of an Art Teaching Quality Evaluation System Based on Big Data and an Association Rule Algorithm from the Perspective of Sustainable Development,” Soft Computing, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mustafa Yağcı, “Educational Data Mining: Prediction of Students' Academic Performance Using Machine Learning Algorithms,” Smart Learning Environments, vol. 9, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Xu Xin et al., “Review on A Big Data-Based Innovative Knowledge Teaching Evaluation System in Universities,” Journal of Innovation & Knowledge, vol. 7, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ashraf Alam, and Atasi Mohanty, “Predicting Students’ Performance Employing Educational Data Mining Techniques, Machine Learning, and Learning Analytics,” Communication, Networks and Computing, pp. 166-177, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Imran Rashid Banday et al., “Big Data in Academia: A Proposed Framework for Improving Students Performance,” Revue d'Intelligence Artificielle, vol. 36, no. 4, pp. 589-595, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Jin He, Kuo-Yi Lin, and Ya Dai, “A Data-Driven Innovation Model of Big Data Digital Learning and Its Empirical Study,” Information Dynamics and Applications, vol. 1, no. 1, pp. 35-43, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Nasrin Shabani et al., “A Rule-Based Approach for Mining Creative Thinking Patterns from Big Educational Data,” AppliedMath, vol. 3, no. 1, pp. 243-267, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Carlos A. Palacios et al., “Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile,” Entropy, vol. 23, no. 4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]