WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges


  • Fei Gao Non-governmental Higher Education Institute of China, Zhejiang Shuren College, Hangzhou 310015, China
  • Jing Yang Hebei Women and Children Activity Center, Shijiazhuang 050081, China
  • Yang Yang School of Transportation Science and Engineering, Beihang University Beijing 100191, China
  • Xiaojing Yuan School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China




WOMDI-Apriori, data mining algorithm, clustered indicators analysis, specialty groups, higher vocational colleges


The cluster effect of specialty groups plays an important role in the development of Higher Vocational Colleges. The purpose of this research is to scientifically explore the interaction mech- anism of specialty groups clustering indexes in higher vocational colleges, uantitatively analyze the correlation of these indexes, nd explore reasonable measures to promote the specialty groups clustering effect in higher ocational olleges. Firstly, data denoising and field screening were car- ried out on the original data, and then the variables were clustered and divided into LHS (Left Hand Side) and RHS (Right Hand Side). Then, an improved multi-dimensional interactive Apri- ori association rule mining algorithm considering index weights and orientation constraints was proposed. The improved Apriori algorithm and the traditional Apriori algorithm were applied to mine the structured data sets. The results show that the improved WOMDI-Apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional Apriori algorithm. The results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; The majr category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart mployment rate; for association rules where the uccessor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. For association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. The framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges.


Camilleri, A., Delplace, S., Frankowicz, M. et al.(2014) . Professional Higher Education in Europe Characteristics, Practice Examples and National Differences, Brussels: European Association of Institutions in Higher Education. 2014.

Grubb , W., Badway, N., Bell, D.,Kraskouskas, E.(1996). Community College Innovations in Workforce Preparation: Curriculum Integration and Tech-Prep, Washington, DC: Office of Vocational and Adult Education.1996.

Gu, Y.A.(2016). Applied Undergraduate Specialty Cluster: An Important Breakthrough in the Transformation and Development of Local Universities, China Higher Education, 22, 35-38, 2016.

Zeng, X.W., Zhang, S.(2010). On the Construction of Specialty Group in Higher Vocational Colleges - a Qualitative Discussion. Contemporary Education Science, 13, 15-18, 2010.

Zeng, X.W., Yan, M.(2010). On the Construction of Specialty Group in Higher Vocational Colleges - based of Quantitative Analysis, Chinese Vocational and Technical Education, 18, 33-36, 2010.

Zong, C. (2020). Vocational Colleges and Universities: How to Build and How to Evaluate, Journal of Vocational Education, 7, 40-45, 2020.

Zhao, M.C. (2020). On the Nature of Major Clusters Construction of Higher Vocational Colleges and its Organizational Reform Ways on Micro Level, Research in Educational Development, 9, 63-70, 2020.

Yang, Y., He, K., Wang, Y.P. et al.(2022). Identification of Dynamic Traffic Crash Risk for Cross-area Freeways Based on Statistical and Machine Learning Methods, Physica A: Statistical Mechanics and Its Applications, 595, 127083-,2022.


Xu, W., Sun, H.Y., Awaga, A.L., Yan, Y.,Cui, Y.J. (2022). Optimization Approaches for Solving Production Scheduling Problem: A Brief Overview and a Case Study for Hybrid Flow Shop Using Genetic Algorithms, Advances in Production Engineering & Management, 17(1), 45-56, 2022.


Sun, H.Y., Xu, W., Yu, Y.Y., Cai , G.Y.(2022). An Intelligent Mechanism for COVID-19 Emergency Resource Coordination and Follow-Up Response, Computational Intelligence and Neuroscience, 1-10.2022.


Cicea, C., Lefteris, T., Marinescu, C., Popa, S, c., Albu, Fc.(2021). Applying Text Mining Technique on Innovation-Development Relationship: A Joint Research Agenda, Economic Computation And Economic Cybernetics Studies And Research, 55(1), 5-22, 2021.


Sousa, Junior W.T. de, Montevechi, J.A.B., Miranda, R. de C., Rocha, F., Vilela, F.F.(2019). Economic Lot-Size Using Machine Learning, Parallelism, Metaheuristic and Simulation, International Journal of Simulation Modelling, 18(2), 205-216, 2019.


Teoh, C.W., Ho, S.B., Dollmat, K.S. et al. (2022). Predicting Student Performance from Video- Based Learning System: a Case Study, Informatics and Service Science, 9(3), 64-7, 2022.

Singh, P.K., Othman, E., Ahmed, R.et al.(2021). Optimized Recommendations by User Profiling Using Apriori Algorithm, Applied Soft Computing, C, 107272, 2021.


Redhu, S., Hegde, R.M. (2020). Optimal Relay Node Selection in Time-varying IoT Networks Using Apriori Contact Pattern Information, Ad hoc networks, 98(Mar.):102065.1-102065.9.2020.


Yang, Y., Wang, K., Yuan, Z., Liu, D. (2022). Predicting Freeway Traffic Crash Severity Using XGBoost-Bayesian Network Model with Consideration of Features Interaction, Journal of Advanced Transportation, 4257865.2022.


Karimtabar, N., Fard, M.J.S.(2022). Finding Frequent Items: Novel Method For Improving Apriori Algorithm, Computer Science-AGH, 23(2), 161-177, 2022.


Pan, T. (2021). An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students, International Journal of Emerging Technologies in Learning, 16(9): 235-246, 2021.


Yang,Y., Tian,N., Wang,Y., Yuan, Z. (2022). A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data, International Journal of Computers Communications & Control, 17(4): 4806.2022.


Yang, Y., Yuan, Z., Meng, R. (2022). Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach, Journal of Transportation Engineering Part A Systems, 148(9): 04022052.2022.


Yang, Y., Yuan, Z., Chen, J., Guo, M. (2017). Assessment of Osculating Value Method Based on Entropy Weight to Transportation Energy Conservation and Emission Reduction, Environmental Engineering & Management Journal, 16(10), 2413-2424, 2017.


Narváez-Bandera, I., Suárez-Gómez, D., Isaza, C E. et al. (2022). Multiple Criteria Optimization (MCO): A Gene Selection Deterministic Tool in RStudio, PLOS ONE, 17. 2022.


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