Articles
| Open Access | KNOWLEDGE EMPOWERMENT: A FRAMEWORK FOR INFORMED DECISION-MAKING THROUGH ENHANCED KNOWLEDGE DISCOVERY
Muhammad Abdul Desa , Malaysian Institute of Industrial Technology (MITEC), University Kuala Lumpur, Persiaran Sinaran Ilmu, Bandar Seri Alam, Masai, Johor, MalaysiaAbstract
In an era characterized by the deluge of data, efficient knowledge discovery is imperative for informed decision-making across diverse domains. This study introduces an innovative framework, "Knowledge Empowerment," designed to enhance knowledge discovery processes. By integrating advanced data analytics, machine learning techniques, and domain-specific expertise, this framework provides a robust foundation for extracting actionable insights from vast datasets. The results empower decision-makers with a deeper understanding of trends, patterns, and hidden knowledge, fostering more informed and strategic decision-making.
Keywords
Knowledge Discovery, Data Analytics, Machine Learning
References
Fayyad, U., G. Piatetsky-Shapiro and P. Smyth, 1996. From data mining to knowledge discovery in databases. AI Mag., 17: 37-54.
Huang, A., L. Zhang, Z. Zhu and Y. Shi, 2009. Data Mining Integrated with Domain Knowledge. In: Cutting-Edge Research Topics on Multiple Criteria Decision Making, Shi, Y., S. Wang, Y. Peng, J. Li and Y. Zeng (Eds.). Springer, New York, USA., ISBN: 9783642022982, pp: 184-187.
Wang, W., H. Chen and M.C. Bell, 2005. Vehicle breakdown duration modelling. J. Trans. Stat., 8: 75-84.
Zhu, Z., J. Gu, L. Zhang, W. Song and R. Gao, 2009. Research on domain-driven actionable knowledge discovery. Commun. Comput. Inform. Sci., 35: 176-183.
Cao, L., D. Luo and C. Zhang, 2007. Knowledge actionability: Satisfying technical and business interestingness. Int. J. Bus. Intell. Data Mining, 2: 496-514.
Luo, D., L. Cao, C. Luo, C. Zhang and W. Wang, 2008. Towards business interestingness in actionable knowledge discovery. Proceedings of the Conference on Applications of Data Mining in e-Business and Finance, June 26, 2008, Amsterdam, The Netherlands, pp: 677-692.
Barai, S.K., 2003. Data mining applications in transportation engineering. Transport, 18: 216-223.
Chen, C.Y., T.Y. Chou, C.Y. Mu, B.J. Lee, M. Chandramouli and H. Chao, 2005. Using data mining techniques on fleet management system. Proceedings of the 25th Annual Esri International User Conference, July 25-29, 2005, San Diego, California, pp: 1-10.
Gomez-Perez, A., 2004. Ontology Evaluation. In: Handbook on Ontologies, Staab, S. and R. Studer (Eds.). Springer, Berlin, Germany, pp: 251-273.
Cao, L. and C. Zhang, 2007. The evolution of KDD: Towards domain-driven data mining. Int. J. Pattern Recognit. Artif. Intell., 21: 677-692.